The Professional Risk Managers' Guide to the Energy Market (PRMIA Risk Management Series)

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THE PROFESSIONAL RISK MANAGERS’ GUIDE TO THE ENERGY MARKET

Other PRMIA Institute Books The Professional Risk Managers’ Guide to Finance Theory Edited by Carol Alexander and Elizabeth Sheedy The Professional Risk Managers’ Guide to Financial Instruments Edited by Carol Alexander and Elizabeth Sheedy The Professional Risk Managers’ Guide to Financial Markets Edited by Carol Alexander and Elizabeth Sheedy

THE PROFESSIONAL RISK MANAGERS’ GUIDE TO THE ENERGY MARKET

Edited by PETER C. FUSARO

New York Chicago San Francisco Lisbon London Madrid Mexico City Milan New Delhi San Juan Seoul Singapore Sydney Toronto

Copyright © 2008 by PRMIA Institute. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. ISBN: 978-0-07-163165-5 MHID: 0-07-163165-8 The material in this eBook also appears in the print version of this title: ISBN: 978-0-07-154651-5, MHID: 0-07-154651-0. All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill eBooks are available at special quantity discounts to use as premiums and sales promotions, or for use in corporate training programs. To contact a representative please e-mail us at [email protected]. This publication is designed to provide accurate and authoritative information in regard to the subject matter covered. It is sold with the understanding that neither the author nor the publisher is engaged in rendering legal, accounting, futures/securities trading, or other professional service. If legal advice or other expert assistance is required, the services of a competent professional person should be sought. —From a Declaration of Principles jointly adopted by a Committee of the American Bar Association and a Committee of Publishers TERMS OF USE This is a copyrighted work and The McGraw-Hill Companies, Inc. (“McGrawHill”) and its licensors reserve all rights in and to the work. Use of this work is subject to these terms. Except as permitted under the Copyright Act of 1976 and the right to store and retrieve one copy of the work, you may not decompile, disassemble, reverse engineer, reproduce, modify, create derivative works based upon, transmit, distribute, disseminate, sell, publish or sublicense the work or any part of it without McGraw-Hill’s prior consent. You may use the work for your own noncommercial and personal use; any other use of the work is strictly prohibited. Your right to use the work may be terminated if you fail to comply with these terms. THE WORK IS PROVIDED “AS IS.” McGRAW-HILL AND ITS LICENSORS MAKE NO GUARANTEES OR WARRANTIES AS TO THE ACCURACY, ADEQUACY OR COMPLETENESS OF OR RESULTS TO BE OBTAINED FROM USING THE WORK, INCLUDING ANY INFORMATION THAT CAN BE ACCESSED THROUGH THE WORK VIA HYPERLINK OR OTHERWISE, AND EXPRESSLY DISCLAIM ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. McGraw-Hill and its licensors do not warrant or guarantee that the functions contained in the work will meet your requirements or that its operation will be uninterrupted or error free. Neither McGraw-Hill nor its licensors shall be liable to you or anyone else for any inaccuracy, error or omission, regardless of cause, in the work or for any damages resulting therefrom. McGraw-Hill has no responsibility for the content of any information accessed through the work. Under no circumstances shall McGraw-Hill and/or its licensors be liable for any indirect, incidental, special, punitive, consequential or similar damages that result from the use of or inability to use the work, even if any of them has been advised of the possibility of such damages. This limitation of liability shall apply to any claim or cause whatsoever whether such claim or cause arises in contract, tort or otherwise.

D E D I C A T I O N

PRMIA Publications, part of the PRMIA Institute (http://prmia.org/ INDEX/institute01/) wishes to express its deepest gratitude to Peter C. Fusaro, our editor, and the authors of the Professional Risk Managers’ Guide to Energy and Environmental Markets. The effort required to assemble such a diverse set of authors into a cohesive text is significant. Yet, the publication of this work comes at a time when energy and environmental risks are affecting markets around the world. This book is, therefore, essential reading for all financial risk managers, regardless of their industry focus. We would also like to thank Richard Leigh, our London-based copy editor, Holly Thesieres, our US-based layout editor and Professor Carol Alexander, the Executive Editor of PRMIA Publications, for their dedication and professional work. We trust you will find that reading this publication enriches your understanding of these important markets and that this book proves to be an invaluable reference tool for you in the years to come. David R. Koenig Executive Director, Professional Risk Managers’ International Association (PRMIA).

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C O N T E N T S

DEDICATION v ABOUT THE CONTRIBUTORS xvii Chapter 1

Introduction to Energy Financial Risk Management 1 Introduction 1 Energy is an Immature Financial Market 3 The Market Drivers of Energy Trading 3 Organization of this Book 5 Chapter 2

Energy Futures Today 7 Introduction 7 Futures: Where’s the Risk? 8 Risk Premiums 10 The New Market Fundamentals 11 The Future of Energy Futures 14 Notes 16 Chapter 3

Overview of the Over-the-Counter Energy Derivatives Market 17 Introduction 17 Overview of Energy Markets 18 OTC Trading 20 OTC Instruments 22 The Convergence of OTC and Futures 24 Futures Contracts Settlement on Expiry 24 Settlement of Swaps Contracts on Expiry 25 Where Have OTC Trading and Clearing Platform Progression Left the Market? 28

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Contents

A Short History of Energy Trading 28 Conclusions 30 Note 30 Chapter 4

Energy Derivatives Structures 31 Risk and Risk Management Themes 31 Market Risk Exposure 33 Basic Derivatives Instruments 35 Derivatives Packages 42 Structuring and Derivatives Structures 46 Reference 50 Chapter 5

The Nordic Electricity Markets 51 Introduction 51 The Nordic Electricity Market 51 History and Development of the Nordic Electricity Market 55 Nord Pool Spot: The Physical Day-Ahead Market 56 Determination of Day-Ahead Market Clearing Prices 56 Area Prices and Market Characteristics 57 Settlement 59 The Electricity Derivatives Market: Nord Pool Financial Market 60 Counterparty Risk 62 The Emissions Market: Nord Pool Carbon Dioxide Allowance Market 63 Conclusions 65 Chapter 6

Market Risk Measurement and Management for Energy Firms 67 Introduction 67 Measure of Market Risk 68 Estimating Measures of Market Risk with a Mean-Reverting Jump-Diffusion Process 71 Modeling Spreads 74 Stress Tests and Scenario Analysis 77 Organizational and Qualitative Aspects of Risk Management 78

Contents

References 79 Notes 79 Chapter 7

Best Practices in Credit Risk Management for Energy and Commodity Derivatives 81 Introduction 81 Internal Risk Ratings: Obtaining and Analyzing Credit-Related Information from Counterparties 82 How Bad Can It Get? Potential Future Exposure 83 Counterparty Credit Risk Charges 88 Credit Loss Distributions 88 Economic Capital and Credit Risk 89 Conclusions 93 References 93 Chapter 8

Introduction to Natural Gas Trading 95 What Is Natural Gas? 95 Consumption Commodity 96 Pipeline Grid 96 Supply and Demand 97 Financial Market 98 The Physical Market Meets the Financial 101 Characteristics of Natural Gas Risk 104 How the US Gas Industry Developed 106 Where Are We Going? 108 Chapter 9

Structured Transactions in Natural Gas 111 Introduction 111 Natural Gas Storage 112 Valuation Techniques 112 Optimization Overview 121 Swing Options 124 Asian Options 127

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Swaptions 129 References 130 Notes 130 Chapter 10

Liquidity Risk Measurement and Management for Energy Firms 133 Introduction 133 Components of a Liquidity Risk Framework 133 Infrastructure 140 Conclusions 143 References 143 Chapter 11

Value of Technical Analysis in Energy Markets 145 Introduction 145 What Is Technical Analysis? 146 The Principles of Technical Analysis 147 Trendlines 148 Trendline and Breakout 150 Other Types of Charts 152 End of Trend Signal 155 Fibonacci Retracement Levels 157 Chart Reading 159 Mathematical Indicators 159 Interpretation 162 Conclusions 164 Reference 165 Chapter 12

Risk Management in Energy-Focused Commodity Futures Investing 167 Introduction 167 Risk Is the Flipside of Return 167 The Most Important Element of an Investment Process 168 Product Design Issues 168

Contents

Contents

Viability of a Futures Program 169 Standard Risk Management Methodology 169 Understanding Price Behavior 170 Value at Risk 171 Scenario Testing 172 Deep Out-of-the-Money Options 173 Exit Strategy 173 Diversification and Concentration Risk 173 Understanding the Fundamental Drivers of a Strategy 174 Extraordinary Stress Testing 180 Risk Management Reports 182 Conclusions 183 Acknowledgements 184 References 185 Chapter 13

The ISDA Master Agreement Ten Years On, ISDA 2002 187 Introduction 187 The ISDA Agreement 187 The ISDA Master Agreement 189 Useful ISDA Publications 189 Pre-confirmations and Long-Form Confirmations 190 ISDA Documentation Processing 191 Trading Before an ISDA Is Signed 194 The Main Differences between ISDA 2002 and the ISDA 1992 Master Agreement 195 Appendix A: ISDA Agreement 201 Appendix B: Sample Letter 206 Notes 207 Chapter 14

Creation and Transfer of Price Risk in European Energy Markets 209 Introduction 209 Creation of Risk 210

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Contents

Risk Transfer 213 Financial Risk Transfer 219 The Impact of Policy and Regulation 223 Conclusions 225 References 227 Notes 227 Chapter 15

Energy Options 101 231 Introduction 231 Basics of Oil Derivatives Structures (Exchange Traded and Over-the-Counter) 233 Basic Derivatives Structures 234 Option Pricing Methodology/Overview of Option Pricing 238 Conclusions 242 Notes 243 Chapter 16

Energy Trading, Transaction, and Risk Management Software: A Key Component in Risk Management 245 Introduction 245 Historical Perspectives 245 Current Status of ETRM Software 247 A Dichotomy of Requirements 250 A New Era of ETRM Software? 251 ETRM Software as an Essential Part of Risk Management Policy 252 Risk Management Tools and Methods 253 Conclusions 254 References 254 Chapter 17

Electricity Options 255 Introduction 255 History of Electricity Options 256 Who Uses Electricity Options? 257 Option Basics 257 The Greeks 258

Contents

The Valuation of Options 259 Electricity Option Basic Structures 261 Commonly Observed OTC Electricity Option Products 262 Potential Portfolio Applications 265 Conclusions 268 Notes 268 Chapter 18

The New Weather Risk Market Hedging and Trading Strategies 269 Why Is Weather Important? 269 Developing a Weather Strategy: How to Identify and Quantify the Risk 271 Developing a Weather Strategy 276 Conclusions 281 Further Information 282 Notes 282 Chapter 19

Outlook for Asian Energy Markets 283 Introduction 283 Asia’s Risk Profile 284 Different Market Evolution 284 The Market Drivers of Energy Trading 285 Market Development 285 The Asia-Pacific Region in the Global Supply Scheme 286 Other Fundamental Changes Under Way in Asian Oil Markets 288 Changes in Oil Suppliers 289 Tanker Market Developments 289 Asian Market Characteristics 290 Challenges to Change 291 Conclusions 293 Chapter 20

Green Trading: Environmental Financial Markets and Energy Trading 295 Introduction 295 Market Developments Now Under Way 297

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Where is Green Trading Today? 299 The New Investment Model for the Green Space 301 Climate Change as an Investment Opportunity 303 Cleantech Investment Indexes 304 Get to Know Your Risks 304 Why Environment Is Rising as Both a Corporate Financial Issue and Investment Opportunity 305 Chapter 21

Lessons Learned from the US Experience in Trading Sulfur Dioxide Allowances 307 Introduction 307 Summary of SO2 Allowance Trading 308 Experience to Date 311 Lessons Learned 317 References 324 Chapter 22

The Complexities of Trading Regional Emission Markets 327 Introduction 327 Houston/Galveston Mass Emissions Cap and Trade (MECT) Program 330 San Joaquin Valley Emission Reduction Credit Trading Program 333 Notes 336 Chapter 23

Climate Risks and Electric Utilities 337 Introduction 337 States Taking the Lead in the USA 340 Focus on Electric Power Companies 341 IGCC Holds Promise 348 Outlook for US Carbon Regime 352 References 353 Notes 353

Contents

Contents

Chapter 24

Green, White, and Red Certificates Trading in Italy 355 Introduction 355 Green Trading 356 White Trading 358 Red Trading 360 Conclusions 362 Note 362 Chapter 25

Carbon Trading: A New Commodity Is Born 363 Introduction 363 The Genesis of Carbon 364 The Policy Framework 366 The Kyoto Protocol 367 Flexibility Mechanisms 368 The EU Emissions Trading Scheme 375 Drivers of Carbon Pricing in the EU ETS 377 Future Uncertainties in European Gas and Power Affecting Carbon Pricing 383 Emergence of Multiple Carbon Product Markets in the Short Term 384 Will the EU ETS Set the Global Price for Carbon? 384 Impact on the European Power Sector 387 Concluding Remarks: Post-2012 Scenario 390 Chapter 26

Entrance of Energy and Environmental Hedge Funds 393 Introduction 393 Energy Hedge Funds 394 Why Enter Energy Now? 396 Oil Trading Market Opportunities 399 Structural Changes in Commodity Trading 399 Waiting for Mean Price Reversion 400 What Is a Hedge Fund? 402

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Contents

Types of Hedge Funds 403 Why the Hedge Fund Factor Is Here to Stay 405 How Are Oil and Gas Prices Determined? 405 Energy Trading Is Now Rebuilt 407 What the Future Holds 410 Acknowledgments 412 References 412 Note 412 Chapter 27

Forward-Looking Energy and Environmental Trading Market Developments 413 Introduction 413 Energy Hedge Funds 417 Nature of Risk-Taking Is Changing 418 Other Market Changes 419 The Future in Energy and Environmental Trading Will Be Different 420 Conclusions 420

GLOSSARY 423 INDEX 451

A B O U T

T H E

C O N T R I B U T O R S

Stefano Alaimo is responsible for Environmental Markets at Gestore Mercato Elettrico in Rome, Italy. José Ramón Aragonés, Ph.D., is Professor of Finance at Universidad Complutense, Madrid, Spain, and Hedge Fund Research Director at Black Swan Risk Advisors, LLC, Berkeley, California. Carlos Blanco, Ph.D., is Managing Director of Black Swan Risk Advisors, LLC, Berkeley, California. Kevin Dowd, Ph.D., is Professor of Financial Risk Management at Nottingham University Business School, UK, and Director of Research at Black Swan Risk Advisors, LLC, Berkeley, California. Åsmund Drivenes works at Nord Pool in Oslo, Norway. Peter C. Fusaro is Chairman of the energy and environmental advisory firm, Global Change Associates Inc. (www.global-change.com) in New York. Peter is author of the New York Times best seller What Went Wrong at Enron as well as 12 other books on energy and environmental financial markets. He is an active keynote speaker and lecturer on environmental financial markets and carbon trading. He wrote the first book on energy risk management called Energy Risk Management (McGraw-Hill, 1998) Peter coined the term “green trading” in 2002 to describe the convergence of environment and the capital markets and holds the annual Wall Street Green Trading Summit (www.wsgts.com) in New York each spring. He co-founded the Energy Hedge Fund Center LLC (www.energyhedgefunds.com) in 2004. His email is peterfusaro@ global-change.com. Frank Hayden has over 18 years of industry experience. During that time he has both traded and managed risk in the energy arena. Mr. Hayden worked closely with the NYMEX on the development of the natural gas futures contract and was one of the first participants to trade natural gas xvii

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About the Contributors

futures on opening day. Mr. Hayden has both consulted and directed the risk assessment at a number of large energy companies. He is currently Managing Director for market risk at Cinergy. Mark Houldsworth holds a Ph.D. in Economics from Michigan State University, where he is an advisor for the Financial Analysis Laboratory at the Broad School of Business. His interests include energy asset dynamic optimization and real time statistical pattern recognition for energy asset behavior. Dr. Houldsworth’s experience includes gas trading, structuring, and building derivative models for both power and gas. He is currently Director of Risk Analytics at Duke Energy. Tom James has been involved in energy and commodity markets since 1989 and is currently Head of Commodity Trading for the firm Liquid Capital (www.liquidcapital.com). Tom has been advisor to numerous energy firms in Asia and Europe. In early 2006 he was appointed Chair Professor of Natural Gas markets at the University of Petroleum & Energy Studies (www.upesindia.org) and whilst being a practitioner in the market is also a published researcher and author of several books, including Energy Price Risk (Palgrave Macmillan, 2003), co-author of Energy Hedging in Asia (Palgrave Macmillan, 2005), co-author of Energy and Emissions Markets, Collision or Convergence (John Wiley & Sons, 2006) and author of Energy Markets (Wiley Finance, 2007). Kevin Kremke is Director of Investor Relations at Reliant Energy, Houston, Texas. Rob Kristufek is the Managing Director of Quiet Light Trading, LLC, Dallas, Texas, where he heads energy trading. He previously worked as a trader and manager at several energy trading firms, including TXU Energy, Hafslund Energy, Avista Energy, and El Paso Energy Trading. Rob also was a self-employed trader on the NYMEX, where he was a local options market maker in the Natural Gas and Crude Oil pits. He received a B.A. degree in Applied Mathematics from Harvard University. Randall Lack is Managing Partner and Founder of Element Markets, LLC, Houston, Texas, an emissions and renewable brokerage group. Mr. Lack is an acclaimed speaker and media resource for renewable energy


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credits, greenhouse gas trading, pending emissions legislation, and regional emission markets. He has developed regional emissions and greenhouse gas portfolios for a wide range of clients, from municipalities to Fortune 500 corporations. Mr. Lack is a graduate from the University of Houston, holding a B.S. in Marketing and Finance. Carl Larry is an experienced energy and foreign exchange futures broker, and is presently Vice President, Energy Commodities at Citibank in Houston, Texas. Per Otto Larsen works for Nord Pool in Oslo, Norway, Steve Leppard, Ph.D., is Head of Commodity Structuring at Merrill Lynch, in London, UK. Per Christer Lund, Ph.D., is a diplomat at the Norwegian Embassy in Tokyo, Japan and formerly was SVP at Nord Pool in Oslo, Norway. Robert M. Mark, Ph.D., is the Chief Executive Officer of Black Diamond in Pleasant Hill, California. Alessandro Mauro is an energy trading specialist and Head of Risk Management at LITASCO in Switzerland. Nedia Miller, Ph.D., Miller CTA, is an international energy options specialist in New York City. She has contributed to Energy Risk Management (McGraw-Hill, 1998), Energy Derivatives Trading Emerging Markets (Energy Publishing Enterprises, 2000) and Energy Convergence: The Beginning of the MultiCommodity Market (Wiley, 2002). Jason Oakes is presently Director of Electricity Options Trading at a major securities firm in New York. He previously held a number of positions at TXU Energy Trading, ranging from Analyst to Asset Manager and Manager of Options Trading. Jason is a graduate of The Cox School of Business at Southern Methodist University in Dallas. Warren Murdoch is a Credit Risk Specialist at BP Oil International, Ltd.

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Brian O’Hearne is Managing Director, Environmental & Commodity Markets, Swiss Re in New York. He was formerly head of weather trading at Aquila in Kansas City and head of the Weather Risk Management Association (www.wrma.org) Ashutosh Shastri is founder of EnerStrat Consulting in the UK. He has worked with a diversity of utilities, oil and gas companies, and industrial groups in the areas of organization, strategy, and operations in Europe, the USA, Asia, and the Middle East. Ashutosh specializes in the areas of market design, regulatory strategy, operational cost minimization, and energy trading. He has a keen interest in International Affairs and is actively involved with the Sustainable Development Programme of Chatham House in London. Richard T. Stuebi is President of Next Wave Energy (www.nextwaveenergy.com) and an expert on environmental financial markets and new energy technology. He presently is BP Fellow for Energy and Environmental Advancement at the Cleveland Foundation in Cleveland, Ohio. Carla Tabossi was a Senior Research Analyst at Innovest Strategic Investors (www.innovestgroup.com), where her work focused on electric power companies, water supply, and waste management services and airlines. Prior to joining Innovest, she had worked for over five years as a corporate finance analyst at Deutsche Bank and as a strategic planning analyst at ING Bank. A Fulbright Scholar, Ms. Tabossi holds a Master’s degree in International Environmental Policy from the School of International and Public Affairs of Columbia University. A native of Argentina, she holds a first degree in International Relations from Universidad de Belgrano in Buenos Aires, with a focus in economics. Hilary Till is the co-founder of Premia Capital Management, LLC (www.premiacap.com). Premia Capital specializes in detecting pockets of predictability in derivatives markets using statistical techniques. In addition, Ms. Till is an Advisory Board member of the Tellus Natural Resources Fund, a fund of hedge funds, and is also a Research Associate at the EDHEC Risk and Asset Management Research Centre. She has B.A. in Statistics with General Honors from the University of Chicago and an M.Sc. in Statistics from the London School of Economics (LSE).


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She studied at the LSE under a private fellowship administered by the Fulbright Commission. Bjørn Tjomsland works for Nord Pool in Oslo, Norway. Gary M. Vasey, Ph.D., is a Vice President, Utilipoint Europe in Brno, Czech Republic and co-principal of the Energy Hedge Fund Center (www.energyhedgefunds.com).


C H A P T E R

1

Introduction to Energy Financial Risk Management Peter C. Fusaro

INTRODUCTION In February 1991, I set up an energy risk management consultancy, Global Change Associates, in New York City. It may have been the first such consultancy. Initially, I concentrated on changes taking place in oil trading and the maturation of the natural gas market. The New York Mercantile Exchange (NYMEX) had launched its Henry Hub natural gas futures on April 4, 1990. It took several years for that contract to become established. The tipping point was when 25 natural gas producers were encouraged to support the nascent contract by providing liquidity. A pioneering oil and gas company executive undertook that effort. She made that contract work! Previously, the natural gas industry was reluctant to embrace risk management techniques. Producers were reluctant to give upside price appreciation. That changed as gas market prices continued to oscillate, based on pronounced seasonality that still exists today. The better news is that the gas industry made the contract work. That is the key point of energy risk management: the need for energy industry trade participation. It is not enough for knowledgeable money center banks in New York, London, Singapore, and Tokyo to make markets work. Energy risk management requires the active participation of the energy industry. 1

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The Professional Risk Managers’ Guide to Energy Markets

Today, that industry is still growing, and so are energy risk management techniques. Through organizations such as the Professional Risk Managers’ International Association we can see its growth every day. Oil risk management evolved into gas risk management, now on a global scale. Electric power risk management was the next market to evolve. The weather derivatives markets followed that. Looming larger than all these markets is the global environmental financial risk management market for emissions trading. This market began in March 1995 with the US Environmental Protection Agency auction on the Chicago Board of Trade. This annual auction sets limits on the “cap and trade” for sulfur dioxide (SO2) emissions that cause acid rain. Market-based solutions for the environment are a natural outgrowth of commodity markets. Today, we have a growing carbon dioxide (CO2) market that will eventually be bigger than oil market trading. There is an evident cross-commodity market arbitrage with fossil fuel contracts. Trading is developing quickly in this arena too. These are precursors to the growth of the next financial market in water trading. Risk management techniques borrowed from the financial markets have catalyzed both energy and environmental trading. This book is the collective wisdom of many energy and environmental market practitioners. Some of these professionals were pioneers in market development. The intention of this book is to show the evolution, tools, scope, and breadth of the energy and environmental financial markets. For, in mid2006, we have entered the 26th year of energy financial trading, initiated on the NYMEX in 1978 with a No. 2 heating oil contract. The market remains financially immature with a $2.2 trillion notional value for all outstanding contracts, as estimated by the Energy Hedge Fund Center (www.energyhedgefunds.com). That compares to the $1.5 trillion in daily trade in foreign exchange (Forex). Energy has a long way to grow, and so do the environmental financial markets. In fact, the entrance of hundreds of energy hedge funds since 2004 is accelerating the financialization of the energy and environmental markets. It is hoped that this handbook will facilitate some of the knowledge transfer and market growth that is needed to manage risk, monetize assets, and apply financial engineering to all parts of the energy and environmental value chain. The world is ripe for fundamental change in its trading and risk profile. Higher energy prices, deregulation and globalization of markets, technology shifts, and environmental risks have added more price risk than ever before.

CHAPTER 1 Introduction to Energy Financial Risk Management

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ENERGY IS AN IMMATURE FINANCIAL MARKET The energy markets are changing in many ways, both physical and financial. Trying to trade these markets with old playbooks does not work. We now have trading patterns in markets such as heating oil prices rising above gasoline during June 2005 for the first time ever, and in August 2005, natural gas spiked in before the traditional shoulder period of lower demand due to the event risk of hurricanes. The continued run-up in crude, gasoline, heating oil, and natural gas prices during 2006 were harbingers of the future. The energy industry is still too shell-shocked from the price crashes of 1986 and 1998 to even believe it. So, energy companies cough back the profits to investors in dividend boosts and stock buybacks. The new energy hedge fund phenomena will accelerate market transformation. Another “new” business strategy is to go into financial electricity markets with physical assets. If Goldman Sachs and Bear Stearns both own generation assets, then they must know something. That “something” is price discovery in illiquid markets and the ability to backstop power trades with physical assets. This is a key component of arbitrage strategies. The entrance of Morgan Stanley into the West Coast physical jet fuel market is further evidence that Wall Street is entering the physical markets. The expectations are that money center banks in both New York and Houston as well as energy hedge and private equity funds will continue the movement into the physical energy markets. The distinction between physical and financial energy markets is starting to blur. The need to own or have an option on physical capacity of oil, gas, or electric power will make this market maturation process different than anyone expects. The new dynamics are faster-trading markets, more intraday price volatility, and more risk. The realization is that energy risk management has become a fiduciary responsibility of energy companies. THE MARKET DRIVERS OF ENERGY TRADING While energy trading and the use of energy risk management tools have been slow to evolve in Asian energy trading, that present state of affairs is beginning to change across the board in the energy complex. Driven by energy market deregulation, globalization, and privatization trends in many countries, risk is becoming pervasive. As many Asian countries move toward open markets, competitive forces are coalescing that will

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force much more active energy risk management. It can be argued that risk is endemic in market economies. Therefore risk management techniques become the necessary survival skills of Asian corporations. Active energy risk management then becomes a fiduciary responsibility of Asian energy companies. While short-term physical oil trading has always existed in most Asian countries, the energy complex is broadening to include gas, power, petrochemical, coal, and weather risk management. Lurking on the horizon is emissions trading to reduce plant emissions and reduce greenhouse gas emissions. Asia is primed to embrace the active use of energy derivatives and much more sophisticated trading techniques and financial engineering. Borrowing heavily from the institutional memory of well-developed New York and London capital markets, energy trading and risk management are on an upward trajectory in Asia, fueled by growing oil and gas dependencies and the need for more electric power. Credit risk management, similarly, is an area of exponential growth in Asia. The need to actively manage counterparty risk is highlighted in the wake of the demise of Enron and many US and European trading companies. Deregulation of the electric power industry, in particular, brings these risks into focus. While paper market trading for oil and gas has grown on both established futures exchanges and the over-the-counter forward markets since the early 1990s; electricity paper trading is still in its infancy. Electricity deregulation has driven the commoditization process, and there is convergence of both gas and electricity that has accelerated much more on the physical side of the market than the financial trading of power. In fact, the relationship of natural gas marketers and electric power marketers cannot be understated. However, power marketing is a more demanding market. It is a next-hour, next-day, next-week, and next-month business. Power marketers and traders provide greater efficiency by buying and selling power and transmissions capacity. Electric power is a 168-hour, sevendays-a-week market that changes prices hourly, half-hourly, or quarterhourly. It is the most volatile commodity ever created, with price volatility of over 1,000% in some cases. The transition in the market from monopoly to competitive markets has fundamentally changed how utilities and others buy and sell electricity. The transition to global competitive markets will bring more risk to all involved in the energy complex as well as the emerging environmental risk management complex.

CHAPTER 1 Introduction to Energy Financial Risk Management

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ORGANIZATION OF THIS BOOK This purpose of this book is to describe the fundamental evolution of energy and environmental trading. We have organized it accordingly: ●

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Chapter 2: Carl Larry of Barclays Capital explains how energy futures trading works today. Chapter 3: Tom James of Deloitte Consulting presents an overview of over-the-counter derivatives markets. Chapter 4: Dr. Steve Leppard of bpriskmanager follows that presentation with more examples of energy derivatives structures. Chapter 5: Per Christer Lund and colleagues give an exchange’s overview of Nordic electricity markets. Chapters 6 and 7 are collaborative efforts of several risk management experts, Dr. Carlos Blanco, José Ramón Aragonés, Robert Mark, Kevin Dowd, and Warren Murdoch, on market risk measurement and management for energy firms and best practices in credit risk management for energy and commodity derivatives. Chapters 8 and 9 are two companion chapters on natural gas trading. Frank Hayden provides an introduction, and Dr. Mark Houldsworth provides a more advanced treatment of natural gas structured transactions. Chapter 10: We then return to Carlos Blanco, Robert Mark, Kevin Dowd, and Kevin Kremke’s chapter on liquidity risk measurement and management for energy firms. Chapters 11–15: These chapters dig deeper into energy risk management tools. First, Tom James discusses the value of technical analysis in energy markets. Then Hillary Till of Premia Capital examines risk management in energy-focused commodity futures investing. Tom James returns with a discourse on the ISDA Master Agreement. Alessandro Mauro then writes about creation and transfer of price risk in European energy markets. Finally, Dr. Nedia Miller presents an introduction to the use of energy options. Chapter 16: Risk management software is also very important for maintaining the trading book, deal capture, stress testing, and mark to market, as this chapter by Dr. Gary Vasey of Energy and Environment Capital Management, LLC shows.

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Chapter 17: Returning to commodity trading, Rob Kristufek and Jason Oakes embark on a chapter on electricity options. Chapter 18: Brian O’Hearne of Swiss Re moves into another market with his chapter on the implementation of a weather risk management strategy. Brian is a former head of the Weather Risk Management Association (www.wrma.org). Chapter 19: Peter C. Fusaro and Tom James give their views on Asian energy markets. Together, these authors wrote the first book on Asian energy hedging markets, Energy Hedging in Asia, in 2005. Chapters 20–25: With these chapters the book moves beyond energy risk management. Peter C. Fusaro sets the scene for an examination of environmental financial trading. Then Richard Stuebi, who worked on creating the SO2 trading program, assesses lessons learned from the US experience in trading SO2 allowances. Randall Lack of Element Markets then expands the US emissions trading discussion with a look at the complexities of trading regional emissions markets. Carla Tabossi of Innovest Strategic Investors then assesses climate risks and electric utilities. Stefano Alaimo expands the theme of environmental financial trading with his chapter on green, white, and red certificates trading in Italy. Finally, Ashutosh Shastri provides an overview of carbon markets. Chapter 26: Peter C. Fusaro and Gary Vasey focus on the entrance of energy hedge funds. Chapter 27: The book concludes with a forward-looking chapter on energy and environmental trading market developments by Peter C. Fusaro.

C H A P T E R

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Energy Futures Today Carl Larry

INTRODUCTION Futures have been around for centuries; for example, in the 1840s in Chicago, producers and buyers were looking for a way to “hedge” agricultural commodities. They sought ways to eliminate price risk during transportation, bad weather, or a sway in demand. The commodity market is now a global marketplace that handles nearly 5 billion futures and options contracts every year. Trillions of dollars are traded on a day-to-day basis, revolutionizing the way we look at risk and how it is handled. Starting with simple hedges, the futures markets have now become one of the main sources of speculative investment activity. The growth from pure physical hedging to speculative trading is no more evident than in the energy futures market. The energy futures market just a few years ago was a small addition to the larger futures arena. The biggest players in the futures market were the major oil companies, moving oil tankers, and gasoline barges. Futures were the perfect instrument. The price on the futures market was mostly reflective of actual physical demand and supply numbers. After September 11, 2001, however, the world changed—as did the energy futures market in a spectacular way. During 2001, global events started a domino effect to bring the energy futures market to the forefront of risk management. The election of 7

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George W. Bush brought on a new agenda for America. September 11 shook America’s perception of the Middle East and brought to light a dependency on foreign oil. That dependency continues to grow. The collapse of Enron later that year (December) created a larger market interest in energy futures. The main marketplace for energy futures is the New York Mercantile Exchange (NYMEX). Growth has exploded over the past few years at this exchange. In 2006, NYMEX is expected to increase its trading volume by 24% compared to 2005. On this exchange, crude oil (West Texas Intermediate), natural gas (Henry Hub), unleaded gasoline (New York Harbor), and heating oil (No. 2) are traded. These contracts are specified, based on their grade and delivery points, and are the most commonly used. As we move ahead, we are finding more interest in other contracts that are of different specifications, although most of the liquidity stays within these four successful contracts. In the future, as we continue to grow on a global scale, we will find ourselves trading more area-specific contracts such as Dubai Brent crude, European diesel fuels, and Asian fuel oil. Since the futures market will always be primarily used for physical hedges, the growth of these markets is inevitable. The emergence of foreign countries as economic powers consuming more energy will prompt the move of futures trading to their own demographic purposes. FUTURES: WHERE’S THE RISK? Over the past few years, energy futures have been accepted more favorably by the global energy industry. They have developed well as valuable risk management tools. In the past they were locked in to hedge physical energy movements across the globe. Oil companies from America to Asia were in some form or another using these markets to lock in a solid amount of profit or loss. Through the volatile times of political unrest, weather complications, and physical disruptions, energy futures provided some relief and price protection for both oil producers and consumers. As the global economies started to recover, a shift started in commodity futures. More speculative funds were now looking to use these futures to hedge economic strength and direction. Funds, included as “large speculators” in the weekly US Commodity Futures Trading Commission’s Commitment of Traders report, have been growing quickly, and their participation in the futures markets has

CHAPTER 2 Energy Futures Today

9

changed the risk factors. The combination of these funds and their focus on economic impact has increased the importance of energy. Once overlooked as a purely physical market, they are now regarded as economic indicators as well as a natural inflationary hedge. Energy is now noticed by the global financial community. With the fall of Enron in December 2001, a temporary void was left. As with most arbitrage opportunities, this gap was soon filled. The thought was that another physical producer would step in. However, financial firms were concerned about providing financial backing. The financially savvy realized that the market was open to all players, including those with money and an appetite for alternative investment. Soon, large hedge funds started following commodity indexes, hedging oil versus inherent foreign exchange risks and inflationary interest rate moves. Once deemed the “market of cowboys,” it had now become the “smart money market.” During this shift, energy futures grew in daily volume, open interest, and price volatility. Along the way, producers were sometimes stymied with irrational moves. To a physical trader, a $2.00 move in crude oil futures had meant a major physical disruption: a broken pipeline or ship that had failed to deliver. What was beginning to happen was this: the growth in futures was being increasingly dominated by pure financial interests. During these years the physical market was slow to follow the growth that these large financial investors were seeing. They had been watching the financial markets of the world and had seen that within the next few years, economies would do what they are supposed to do: grow. The pattern was obvious, but for many who physically judged by what was “on the water,” the futures market was becoming irrational. Many traditional hedgers in the US were caught as off guard, as were the international oil organizations. The International Energy Agency in Paris and OPEC were continually quoted in the financial press as being puzzled over large gains in the American energy futures markets. To make a point about how much OPEC was unprepared for the new focus of energy futures, we can look at the average disparity between the OPEC basket price (an average of oil prices from OPEC members and Mexico) and the NYMEX market (Table 2.1). The physical fundamentals had not changed much year on year, but the economics of the world had. Demand was now the main risk factor. Smart money was hedging and trading. Seasonal factors are still a part of the way oil products trade but are now complicated by how these new traders view mitigating issues.

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T A B L E

2.1

Yearly average OPEC basket and NYMEX price (US dollars)

2002 2003 2004 2005*

OPEC Basket Price

NYMEX Price

Difference

$ 24.36 $ 28.10 $ 36.05 $ 47.31

$ 26.15 $ 30.99 $ 41.46 $ 53.52

+1.79 +1.89 +5.41 + 6.21

∗Average as of September 2005. Source: OPEC, NYMEX.

In America, we have not had a new refinery built since 1976. Did no one thinkwe would outgrow this supply capacity constraint? The futures market’s growth has become a passing of the torch to a new era of oil trading and analyzing risk. The new generation of currency, interest rate, and economic growth was now being factored into risk equations. Current prices have premium added in to compensate for these factors. RISK PREMIUMS Over the past few years, as crude oil prices have moved from a $10 to $20 range to a $50 to $70 range, there has been much discussion about valueadded “premiums.” As the USA began to discuss military operations to invade Iraq in 2002, the oil markets started to rise in anticipation. Soon, as prices started to float above $30, many oil fundamentalists were talking about a “war premium.” Since demand was only growing slowly post-9/11, the grind higher was blamed on this premium. As justified as it was at the time, the analysts were underestimating the real reason why these future prices found higher ground: demand growth. Many had stated that futures were holding war premiums as high as $10 to $15. As the threat of oil disruptions started to diminish, the so-called premiums never left the oil markets. During this time, a new global demand dynamic was forming. Demand from China seemed to have taken the world by surprise; a country that was becoming one of the largest economies in the world seemed to emerge from nowhere. Should this really have been a shock? Chinese demand was now the new “premium” and, subsequently, another addition


11

to the skyrocketing futures prices. Funds trading on economic trends caught on to the wave and were entrenched in a profitable uptrend for the futures market. Most were busy pointing fingers at such premiums, while the US economy was continuing to recover from a period of recession. Overall oil demand was growing at 3% per year from 2002 to 2004. This increased demand was starting to make an impact on the supply picture. With this increase, the amount of imported oil products also jumped. These numbers were coming in at a record pace, and as they did, the need to hedge their risk became more important. Risk management for oil became more complex, and energy futures trading was starting to grow because of this. Commercial producers, funds, and financial institutions that were backing these players were now hedging their risk exposure to the emerging commodity markets. A new era of market participants was defining the oil trading markets. The difference now was the fundamentals of the markets. Physical oil movements were still a concern, but what these other participants were watching was as important. THE NEW MARKET FUNDAMENTALS Since the development of the NYMEX crude oil contract in 1983, most of the more important market fundamentals were based on physical activity. Supply was always deemed abundant. Demand was never at a threat to domestic supply. Imports were used almost exclusively as a price barometer. Weather was factored as only affecting winter fuels. Hedge funds that were active in oil futures were easily outnumbered by the commercial participants and were only a small part of market movements. In 1995, crude oil futures had an average of 122 commercial participants. This number compared with an average of only 60 funds in the crude futures market. By the fall of 2005, the number of funds involved exploded to an average of 234 in contrast to an average of 169 commercials, according to the Commitment of Traders report. The basis of what these two different types of speculators were looking at was changing the basic risk fundamentals. Physical risks were mainly grouped into domestic and foreign issues. In the USA, pipeline issues would cause price activity. A problem with delivery of oil to its destination because of pipeline issues would cause the deliverable futures contract to react accordingly. Another common disruption that would move markets is refinery disruption or

12


delivery problems with a gasoline barge. In fact, today, refinery issues have been more common as aging American refineries are put under further stress trying to stretch production. These events drive energy markets. Winter fuel markets would run rampant during colder-than-normal winters. Other weather conditions that would mainly affect winter fuels were hurricanes. Just a few years ago, the main concern with hurricanes was the disruption of natural gas pipelines. In recent years the ferocity of hurricanes has put both natural gas supply and the limited crude production in the Gulf Coast at risk. US dependency on domestic supply has become more crucial as our demand continues to grow. Refineries have also become at risk during these hurricanes. As with all of the oil supply in America, every bit of lost oil supply has caused volatile swings in the energy markets due to the supply tightness of these markets. The new era of futures fundamentals now includes economic and geopolitical risk. The strength of the world economies has increased demand everywhere. Chinese demand was brought under the spotlight as an unforeseen demand factor. The fact that several other Asian countries had started to show signs of economic recovery was overlooked. India, Korea, and the Philippines are just three of the countries that were gaining ground economically and whose oil demand was increasing. Adding to the peaking supply and demand picture in the Asian region, Indonesia (a regional supplier) was dealing with a crumbling oil infrastructure and declining resources. Risk management in these Asian countries had now added a new ingredient to their economic core value. Currency values continued to be measured against the US dollar but could also be hedged against money spent in and on oil products. Funds that were investing in Asian economies could add oil futures to help stabilize risk. This model could then be duplicated in emerging markets in OPEC member countries, Latin American oil-producing countries, and Russia. The usefulness of this concept is that no oil product could ever go to zero. Oil would always have value, and risk could be managed with the respective futures markets. In the USA, major oil statistics were released weekly. There are two reporting agencies for oil reporting: the US Department of Energy and the American Petroleum Institute, the oil industry’s trade association. These statistics remain significant and are widely scrutinized by funds and commercial oil traders. The new dynamic of searching these statistics now is trying to find the economic reflection on demand, rather than supply. Over the past few years the supply picture has become more dependent on imports of all products.


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In 2000 US crude oil imports were averaging just over 9 million barrels per day (b/d). By 2005, this had risen to 10.1 million b/d. In real money terms, that would equate to an increase of approximately $53 billion dollars paid for foreign oil. The opportunities to hedge new oil money for domestic purposes and to hedge country risk had become more apparent. From speculative funds to financial institutions that were backing oil dollars, risk management and their respective tools were getting a new audience and interest. Connecting the dots, economic releases that were once of interest to those concerned with financial risk management now had an impact on oil markets. Demand could be traced to strong employment numbers. Retail sales numbers that had moved the retail equity market now ignited ideas to hedge transportation fuels. The heating oil futures that were mainly of concern to US East Coast homeowners in winter seasons now attracted interest from those trying to manage jet and diesel fuels. Federal Reserve rates were now of utmost importance to large oil companies who needed to focus their available cash reserves while holding oil that had risen in price by nearly $30 over three years. Another new dynamic that has become a risk factor in the energy markets is geopolitical risk. In the aftermath of 9/11, foreign activities are more closely scrutinized. The US invasion of Iraq caused oil markets to run above $40 but has only been part of the reason they continued to climb. During the invasion, the biggest fear was that the oil fields in Iraq would be sabotaged beyond recovery. The unfolding circumstances during America’s tenure in Iraq yielded problems that were unforeseen. The worst-case scenario of Saddam Hussein blowing up the oil fields was unfounded, but the oil infrastructure in Iraq was worse than believed. Also during the following months, there were attacks on oil pipelines, but the effect was not as important as the weakened infrastructure. Many oil experts predicted that by 2006, Iraq would be pumping well over 3 million b/d, with some guessing as high as 5 million. As oil pipelines in Iraq became common targets for terrorists, the idea that bigger installations would be targeted added tension to the markets. This added more risk premium to the futures prices, which was followed by more media hype. With talk of terrorist attacks, Middle Eastern fears became more prevalent, and the media started covering these events alongside the oil movements. The next fundamental of new oil trading was about to be uncovered: the media. Starting with the stock market boom in the 1990s, 24-hour news coverage and Internet news gave the markets and investors more accessibility

14


to trading. The oil markets had become newsworthy. They involved Middle East attacks, foreign oil, and economic growth and affected the common public consumer. America was becoming environmentally aware. The hybrid car was not only an environmental statement, but also a way to save money! Gasoline prices were at a record high. Daily news broadcasts made the public aware of the reasons behind the oil market prices. Oil trading was now in vogue with large investors, financial institutions, commercials, and a growing number of small speculators. THE FUTURE OF ENERGY FUTURES With the explosion and exposure of oil futures markets, the bevy of new participants was looking for ways to manage this risk. The NYMEX is the epicenter of oil futures markets. The open interest at this exchange was expanding quickly, as did its volatility. Instead of markets moving in “tics,” minimum price fluctuations, they were moving in waves of uptrends and downswings. The volume that was being traded was more than anyone on the floor had been used to seeing on a day-to-day basis. This was a boon to the NYMEX and its members, but the market price itself was acting erratically. From 2004 to 2005 NYMEX volume had grown over 24%, and open interest was making records in their products.1 The NYMEX seat price has set a record for any futures exchange in November 2005 at $3.77 million. This number is a far cry from 2000, when a seat had set a record sale at $755,000.2 Obviously, over the past five years the interest in oil futures trading has been peaking. The question facing us is how much more this development can continue. The oil futures market has been exclusive to the NYMEX for years. Only the International Petroleum Exchange (IPE) in London has been able to catch any market share over the past few years. The IPE has been trading the benchmark Brent oil contract since 1988. Consistent with other futures exchanges, the IPE had been trading on an open outcry system (floor trading). In April 2005, the IPE took a new direction in oil futures trading and ended open outcry. This was a radical step for many oil traders, many of whom were just getting used to electronic messaging systems. The idea now was to be able to put the trader in front of the trade. Gone were arguments of volume discrepancies and trades that would be through established bids and offers. From a brokerage perspective, the argument was taken out of the equation. Transparency was obvious, and trading became much more efficient.


15

Many traditionalists argued that this would bring the end of liquidity and emotion. The reality was that many of the new era of traders were patiently waiting for this. Futures volume on the IPE, now called the Intercontinental Exchange (ICE), soared. Several records such as daily (295,501 during August 2005) and monthly (4,181,450 in September 2005) volumes were set after the IPE moved to an electronic platform.3 Moving trading to an electronic market also allowed more sophisticated traders to set their trading systems to trade automatically. This gave traders more freedom and meant they need not be so concerned about slippage and human error. The ICE also started a shift in “over the counter” (OTC) markets. As more established financial institutions and higher prices in energy became more established, the unregulated OTC market became more of a risk. Creditworthiness became important, and the ICE (along with the NYMEX) introduced a slate of OTC products that were backed by the London Clearing House. These hybrid futures contracts would become a boon for both exchanges. In the first year alone the ICE had traded over 1 million contracts via its OTC futures system. These contracts were mainly traded by an outside broker matching two sides of a product. Since their inception, these contracts are still mainly traded by this process but have evolved into open markets in some products. In 2002 the NYMEX also entered this OTC clearing trading market. In similar fashion the NYMEX Clearport system developed into a highvolume trading platform. In 2004, over 12 million contracts were traded on the Clearport platform. In September 2005 the daily volume record was set at 286,146 for Clearport trading. The evolution of electronic trading is happening and continues to develop. The effectiveness of a nearly 24-hour market provides accessibility to all regions and their time zones. As discovered years earlier by the Chicago Mercantile Exchange and its conversion to electronic markets, the world doesn’t trade at the same time. Further evidence of the change in the market mentality has been the new e-miNY contracts for crude oil and natural gas. These contracts, introduced in 2002, have increased volume and open interest each successive year. In November 2005 the e-miNY contract had cleared over 5 million contracts, and daily volume had hit a record of 64,784. New investors and traders were finding the ease and availability of this half-sized contract comfortable. By 2006 the availability of electronic oil markets seemed ready to explode. In Dubai the NYMEX already had plans to establish an open

16


outcry exchange for oil futures. It will have competition from the Dubai Commodity Exchange, which plans to launch its electronic fuel oil futures contract. NYMEX Europe has set forth plans to trade an electronic contract alongside its struggling open outcry futures venture. Competition in the expanding oil futures market is great. The tools of risk management could become more region-specific. With more exchanges turning to an electronic marketplace, risk can easily be measured by the deliverability of the contract and the creditworthiness of the respective exchange. Energy futures have become an instrument to hedge physical barrels, financial outlooks, and foreign investment. Hedging has become optimized to include the commodity markets. Oil is a major part of this investment. Over one year we have seen trading volume in energy increase by 15% and in the USA alone by 32%. These numbers will continue to increase as the liquidity and diversity of energy markets expand. NOTES 1. Futures Industry Association. 2. NYMEX. 3. ICE.

C H A P T E R

3

Overview of the Over-the-Counter Energy Derivatives Market Tom James

INTRODUCTION The financial energy markets have undergone a rapid transformation since the early 1990s. This chapter provides a general overview of the mechanics and participants of over-the-counter (OTC) energy trading, focusing on natural gas and crude oil. It puts into context the current state of the OTC energy markets by outlining the history of energy trading. The general use of and terminology associated with these markets are defined, and the most common types of energy derivatives and pricing models are described. Examples are given to illustrate how OTC derivatives are traded and how the derivatives apply to hedging energy production and consumption. Oil companies, refineries, trading firms, and other intermediaries that are engaged in the physical trading of petroleum and petroleum products are exposed to market prices. The profit structure of many companies is highly unstable, owing to market price volatility. Hedging with derivatives is a method to offset risks caused by petroleum trading and to stabilize earnings. The goal of any hedging program is to help companies achieve the optimal risk profile that balances the benefits of protection against the costs of hedging. OTC markets allow companies a flexible way to hedge their exposure. 17

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OVERVIEW OF ENERGY MARKETS The scope and scale of financial derivatives trading in energy markets around the world has broadened dramatically since the early 1990s. The market was in its infancy when the New York Mercantile Exchange (NYMEX) launched the No. 2 heating oil contract in 1978. Today, traders are able to trade around the clock, almost seven days a week, via electronic platforms such as the Intercontinental Exchange and the NYMEX. In the energy industry, derivatives can be bought and sold in two main ways: on an exchange and over the counter. The former refers to the futures markets, which are found on regulated financial exchanges such the NYMEX and London’s ICE Futures (formerly called the International Petroleum Exchange or IPE, and owned by the Intercontinental Exchange). The OTC market is specific to the non-standardized price swaps and OTC options. These are usually traded directly between two companies (principals, players) in the energy markets. Although the futures markets are important to the energy industry, it relies much more heavily on OTC derivatives. This is because OTC derivatives are customized transactions, whereas their on-exchange counterpart, the “futures” contract, is a standardized contract. In theory, each deal on the OTC market is unique, so it is important to be alert to contract terms, pricing mechanisms, and price reference when using OTC derivatives. Some companies find that the measurement and control of risks can be more difficult with an OTC contract because of the lack of price and liquidity transparency in the OTC market (unlike regulated futures exchanges, which publish public real-time price data), and this can create the possibility of an unexpected loss. There are also sometimes additional legal, credit, and operational risks with OTC derivatives compared to onexchange futures contracts. However, the OTC market remains a popular option for price risk management purposes. Many companies find that there are benefits in the flexibility of an OTC derivative because it can be valued against the same price reference as the energy which is being produced or consumed. Generally, all the key terms of an OTC derivatives deal are negotiable, which means that the pricing reference, the payment terms, and the volume can all be adjusted to suit the counterparties to the deal. This is a benefit if an organization has a very specialized or unique price risk which requires a one-off hedging tool. Basically, anything is possible in the OTC energy markets, although, of course, the price of the derivative instrument

CHAPTER 3 Overview of the Over-the-Counter Energy Derivatives Market

19

quoted to suit a customer’s precise and perhaps esoteric needs may not always be attractive. Fortunately, for risk management purposes, the core energy markets, such as the larger oil, gas, and electricity (power) markets, have some active and fairly standardized OTC contracts. They are standard both in their floating price reference and in the sort of minimum contract volume that would normally be traded. Indeed, the increasing standardization in the plain vanilla OTC markets has led to the development of a number of electronic trading platforms. People often ask why regulated futures exchanges seem to be unable to launch new petroleum futures contracts. The answer is that the needs of the market are already being met by the now well-established and liquid OTC derivatives market. Another supporting factor of this observation is that futures exchanges have been successful in launching futures contracts in both the natural gas and power markets. The reason for this is that the regulated futures markets were launched soon after deregulation, before or at the same time as an OTC market was establishing itself. Energy OTC derivatives markets are far less liquid than most other financial derivatives markets such as interest rate or foreign exchange swaps, accounting for less than 1% of the value outstanding on derivatives markets worldwide. This means that those who take part in energy markets, whether as market makers, traders, or end-users (usually companies with underlying price risk in the energy being hedged either as a producer or consumer), need to have clear policies for derivatives usage, including strong management controls and organizational reporting structures, before derivatives are employed. They should also provide shareholders with information that will put to rest any unjustified fears associated with their company’s use of derivatives. Indeed, as a result of the concerns of regulators and public shareholders around the world, more and more information is now required by international accounting standards. The vast majority of physical transactions and OTC swaps are priced using an industry-recognized publication—Platts, which is a division of McGraw-Hill. Platts publishes a daily assessment of the price of any given crude oil or oil product in any given location, according to its own specifications, and also publishes an assessment of the forward curve. These daily value assessments are based on the aggregated bids and offers from many brokers and dealers around the world during a specified time window for each geographic region—usually toward the end of each business day in each major time zone: Asia (Singapore), Europe (London), and the US (New York, and then the West Coast) (Figure 3.1).

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F I G U R E

3.1

Main oil OTC trading/pricing hubs. Asia: Singapore is the main pricing hub. Europe: Mediterranean, Arabian Gulf, northwest Europe (NWE), and Amsterdam-RotterdamAntwerp (ARA) are the main pricing hubs. USA: New York Harbor, US Gulf Coast, US West Coast (LA Pipeline) are some international reference points for oil markets

Price swaps are usually priced off the monthly average of these Platts assessments and lead to a monthly financial payment equivalent to the difference between the traded fixed price and the calculated average floating price multiplied by the contractual monthly quantity. Only the difference is paid, and there is no exchange of physical energy, and hence no delivery risk. OTC TRADING At one time, it was easy to distinguish the futures market from the OTC market and also to establish the pros and cons of using one or the other. As Figure 3.2 shows, when a risk manager or trader used futures contracts, they knew that the contract would be traded on an exchange, that they


F I G U R E

21

3.2

Basic futures trade transaction flow

would have an account with their futures broker, and that they were operating in a highly regulated market. They could also see the price of the contract on a screen, and they could be sure that the security of the contract and its performance would be guaranteed by the clearinghouse of the exchange. This in turn was guaranteed by “margins” (good faith payments by everyone with a futures position on that particular exchange), plus the funding the exchange raised itself and the funds contributed by its clearing broker members. Margins on a futures exchange can be split into two types: “initial margins” and “variation margins.” Initial margins are the good faith deposit that is placed with the clearinghouse or that a broker finances (at a cost) when a trade is opened. Variation margin is the daily revaluation of a portfolio with the clearinghouse. If the valuation is negative, your broker or you (if you have a credit line) will have to place a margin to cover that negative variation margin. If the next day, the portfolio has a positive variation margin (i.e. it is showing an unrealized profit) because

22


the position has not yet been traded or closed out, some of that margin will be returned. However, when OTC contracts are used, there is always the credit risk of the other company in the transaction as well as a liquidity risk and a lack of price transparency because there is no screen to display a real-time price. OTC INSTRUMENTS Over-the-counter derivatives fall into two categories: swaps and options. A swap is a contractual agreement entered into between two counterparties, under which each agrees to make periodic payments to the other for an agreed period of time based upon a notional amount of volume. Swaps are financially or cash settled, as opposed to physically settled. This means the actual cash amounts are wired between accounts periodically, typically at month’s end. No physical delivery of the commodity is required. In the most common type of commodity swap one party to the transaction will pay a fixed price, while the other party agrees to pay a floating price. The fixed price payer (floating price seller) is the buyer of the swap. Conversely, the fixed price seller (floating price buyer) is the seller of the swap. There are a number of issues that must be considered when a party decides to enter into a swap. These issues include the credit quality of the counterparty, the price rate, the pricing index used, payment dates, and payment frequency. Oil and gas price swaps are usually denominated in dollars. In the crude oil markets, prices are typically denominated in US dollars per barrel. In the natural gas markets, natural gas futures are for delivery of natural gas most commonly benchmarked against Henry Hub spot prices in Louisiana. Prices are quoted in US dollars and cents per million BTU. The contract symbol for Henry Hub natural gas traded on the NYMEX is “NG.” Natural gas prices are highly seasonal. Winter prices typically exceed summer prices because of increased demand during cold weather days. The result is a “peak and valley” pattern. Natural gas prices typically trade up to 10 years forward. For example, in 2006, traders show prices for tenors from 2006 to 2016, and sometimes beyond. OTC trades are done either as calendar strips, summer strips, or winter strips.1 Turning to options, plain vanilla American and European options are well understood and widely used in the energy financial markets.


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American options on crude oil trade on the floor of the NYMEX. Increasingly sophisticated investors have driven the development of more customized and exotic financial instruments. The more exotic options trade in the OTC markets. Exotic options can be complex and difficult to price. A fair valuation is needed in order to trade such instruments. Average price options (APOs), also referred to as Asian options, have payoffs which depend on an average of prices for an underlying commodity over a period of time. These types of options have become very popular in the OTC markets over the past decade, leading to an explosion in the literature dedicated to APOs and available pricing algorithms. Unlike the options that trade on the NYMEX, which are American style and can be exercised into a futures position, APOs are cash settled. The options are not exercised, and the holder of the option does not receive a position in the underlying asset. Instead, counterparties with option positions transfer funds five business days after the end of each month. The payoff is the difference between the strike price and the daily average of the front month contract. In short, the trades are confirmed as “daily average, monthly settle.” The terminology associated with APOs is identical to that of American or European options. APO types are puts, calls, and various combinations of the two. The time periods that typically trade OTC are based on calendar months. In this respect they are different from NYMEX traded options on futures contracts. Traders of APOs speak in terms of calendar months, quarters, and years. APOs are one risk management tool commonly used in the oil markets because of their characteristics. They are cheaper than American or European options because the volatility of the option is reduced by averaging prices during the averaging period. In addition, they are useful in markets that are highly volatile and that are susceptible to temporarily distorted prices. Oil prices especially can experience one-day spikes or sell-offs, but the effect of a single outlier is not as pronounced when averaging all settlement prices over a calendar month. The settlement terms of these types of options simplifies the accounting process. Expiry dates do not need to be tracked; the options settle at the end of each month. The options provide a useful risk management tool because the tenor spans every calendar day during the life of the option. Price risk exposure is therefore covered each day. These characteristics of APOs are the reason why they are very commonly used for hedging in the crude oil markets.

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THE CONVERGENCE OF OTC AND FUTURES The clear distinction between the OTC energy market and the futures market is disappearing as the two markets converge (Figure 3.3). Clearinghouses around the world have started to accept OTC trades into their guarantee umbrella. This means that after executing bilateral OTC trades with one another, both counterparties can agree to “give in” their OTC deal to a clearinghouse. This process makes the clearinghouse the counterparty to the OTC deal so that the two OTC counterparties can benefit from the higher credit quality of the clearinghouse as well as getting other benefits, such as more netting opportunities on settlement and offsetting of positions. The usual market approach is for two OTC counterparties to trade an OTC derivative contract with one another directly and to take on one another’s credit risk. But in the new convergent environment, users can view and trade OTC prices on-screen just like futures markets. The lines are definitely blurring between OTC and futures. FUTURES CONTRACTS SETTLEMENT ON EXPIRY Energy futures contracts all entail physical and cash delivery on expiry (apart from ICE Futures’ Brent crude futures in London, which are cash

F I G U R E

3.3

Convergence of OTC and futures


25

settled). So if a seller (someone who is short in the market) holds the futures contract to expiry, he will have to deliver the underlying physical energy (oil, gas, power), and if a buyer (someone who is long in the market) holds the contract to expiry, he will have to take delivery of the underlying physical energy. However, actual delivery via futures markets such as the NYMEX or ICE Futures is very small, normally less than 2% of the total open interest (the total amount of outstanding contracts in the market). The majority of trades on these markets are for hedging and/or speculative purposes, with consumers or producers of energy preferring to make delivery via the normal physical markets, rather than through the futures markets.

SETTLEMENT OF SWAPS CONTRACTS ON EXPIRY Swaps are contracts which, unlike futures, never go to physical delivery. They are by their very legal structure purely financially based contracts, which allow companies to benefit from the price/value movement of the underlying asset from which the swaps price is derived. They are called swaps because the two counterparties to the deal, the buyer and the seller (the long and the short), exchange an agreed fixed price today for the unknown floating price in the future. When traders are negotiating an OTC deal, they focus on ● ● ● ● ● ●

the fixed price the floating price reference pricing period (e.g. one month, quarterly, calendar year) start date or effective date end date or termination date payment due date.

For example, for a swap priced against an American or European floating price reference, payment due date is normally the fifth business day after the last pricing day of each pricing period. In energy and, generally, commodity markets, OTC derivatives will price out monthly, so even if a quarterly contract is traded, after each month during the pricing period, one third of the volume will price out, and a settlement will become due by or a payment received by the organization. For contracts pricing

26


against an Asian-based floating price reference, payment for settlement is generally due 10 (sometimes up to 14) business days after each pricing period. What happens to an option contract on expiry and when or whether it is exercised depends very much on the type of option it is and also whether it is a futures option (traded on a futures exchange, referred to as traded options) or whether it is an OTC option. There have been few safety nets in the OTC world of derivatives. However, some important safety nets are now being successfully introduced to the OTC energy markets. The most successful is the NYMEX ClearPort system. This allows you to clear a vast array of OTC derivatives priced against, for example, the Platts market indices, which are the most popular amongst market participants (www.platts.com). As a result, you get the best of both worlds, the flexibility of derivatives pricing against the price index you want, while mitigating credit risk in the market by putting your trades via a clearinghouse (also called a “cleared trade”) such as NYMEX, which has AAA rated credit. Cleared trades can be entered on both the Intercontinental Exchange and via NYMEX ClearPort. Removing credit issues enables market participants to deal with one another directly. Previously, credit issues restricted trading. The advent of clearing for OTC derivatives has sparked another revolution in the market. Primarily due to barriers of entry, such as credit rating and lengthy and costly legal documentation, futures and OTC trading during the 1990s was dominated by large financial institutions, oil companies, and floor traders, referred to as “locals.” Now hedge funds have emerged as the dominant force, not only taking large speculative positions on a discretionary basis but also making markets for customers who need to initiate or offset positions. Clearing OTC products on NYMEX ClearPort and ICE has allowed the funds to participate on the same level as other institutional traders. Hedge funds do not face the same VAR (Value at Risk) limits, tight risk controls, and corporate layers of others in the marketplace, allowing them to make quick decisions and trade large volumes. With NYMEX ClearPort, there is no need for multiple documentation as NYMEX acts as the central counterparty to all trades on ClearPort, opening up hundreds of OTC commodity products to new market participants and, in particular, both large and small fund managers. It has created a scenario where hedge funds such as Centaurus and MotherRock could compete alongside J Aron and BP for natural gas business.


27

Convergence is the key and has its seeds in the development of electronic trading platforms in the late 1990s, which enabled efficient, effective, and economical access to a diverse range of products on a single source. The “80/20 rule” applies to OTC swaps markets, where by the late 1990s, around 80% of OTC trades had already become fairly standardized, making them ideally suited to the electronic trading medium. Energy trading has been slow to go electronic, but electronic trading is gaining in popularity. NYMEX does most of its trading in open outcry pits, with after-hours trading platforms using NYMEX Access, which has seen increasing volumes of overnight trades, NYMEX, and OTC trading through ClearPort. Intercontinental Exchange has both OTC and futures offered only electronically. Now NYMEX will offer its futures trading platform both on the floor and electronically during trading hours with its alliance with CME Globex. Despite all these technological shifts, OTC voice brokers still command the majority of business and use phones to close deals. Indeed, the only significant difference between the standardized OTC contracts and the futures contracts was clearing and the mitigation of counterparty credit risk, which had been available to on-exchange futures contracts for several decades. But it was not until the well-publicized implosion of Enron in December 2001, and its impact on credit risk perceptions, that the clearing issue gained more prominence. Equally, Enron’s bankruptcy significantly stimulated the funds’ interest in the energy sector. Enron set the standard for energy trading, which focused on more speculative, investment-based trading as opposed to pure price hedging. Yet at its demise the main debating point was whether energy trading was justified, with some commentators calling for an end to energy trading per se. In effect, the impact of Enron was to remove liquidity from the emerging power market and encourage more asset-based trading, that is to say, pure hedging as opposed to speculation. The consequence was increased price volatility and price risk, which ideally suited funds. A secondary, and significantly more important, legacy of Enron’s demise was the number of energy merchants going out of business, and the significant number of very clever and experienced Enron traders out of a job, in a market that for a short period of time had a surplus of energy traders. Funds and institutions were quick to identify the increased price risks in the energy market caused by Enron’s exit and its domino impact on other energy companies with which it did business, and the opportunities

28


afforded by the inclusion of energy products in their investment portfolio, and quickly moved to harness this energy trading expertise, to assist them in managing their growing energy portfolios. WHERE HAVE OTC TRADING AND CLEARING PLATFORM PROGRESSION LEFT THE MARKET? The energy trading landscape has changed radically. The participants in the energy markets have gone from a few NYMEX locals, housed in the World Trade Center, and a handful of institutional traders (Banker’s Trust was a pioneer then) to a group that is various and diverse. The consequences of these changes have been as follows. ● ●

●

●

●

Record volumes. Huge swings in volatility; volatility is volatile. Volatility is not at record levels but is very fast moving intraday. Unorthodox price movements. The price of front crude will go up $1.50/bbl, while the back of the curve goes down $0.50/bbl. The front end contract is at an all-time high, yet the first six monthly contracts are in contango. Prices are at an all-time high. This has less to do with fundamental factors and more to do with lots of capital chasing few commodities. Gold is also at a high as the equities stagnate. Faster moving markets. Prices can rise $0.50 in five minutes, then give back $0.75 in the next five minutes. Electronic order systems and the increase in speculation have lead to very choppy intraday prices that can reverse themselves instantaneously (though the general direction is higher).

A SHORT HISTORY OF ENERGY TRADING The history of energy trading has not been a smooth progression; rather, the market has lurched forward through a series of drawbacks. Until the 1970s, the price of oil was relatively stable, with production largely controlled by the biggest oil companies. Two oil price shocks in the 1970s meant that price volatility became a fundamental feature of the market, short-term physical markets rapidly evolved, and the need to hedge emerged. The following is a chronology of energy trading.


● ●

●

●

● ● ● ● ●

●

●

●

●

●

29

1974: Rotterdam fuel on contract is launched but quickly scrapped. 1978: No. 2 heating oil futures launched. The deregulation of US prices on heating oil leads the NYMEX to develop a heating oil futures contract. 1980: A group of energy and futures companies found the International Petroleum Exchange (IPE) in London. 1981: The first energy contract on the IPE for gas oil futures is launched. 1983: The NYMEX WTI crude oil contract is launched. 1988: The IPE successfully launches Brent Crude futures. 1990: NYMEX issues natural gas futures contract. 1994: NYMEX and COMEX merge under the NYMEX. 1997: NYMEX and COMEX move to their new building at One North End Avenue, with its larger trading floor, enabling a more sophisticated operation. 2000: US Congress passes the Commodity Futures Modernization Act. This new law creates a flexible structure for regulation of futures trading. 2001: Enron collapses, and the energy merchant sector begins to crumble. Many exit trading: Enron, Mirant, Aquila, El Paso, and Williams. There is a need for stricter corporate governance and risk controls. 2002: The Intercontinental Exchange (ICE) comes into being, an electronic global futures and OTC marketplace for trading energy commodity contracts on an Internet-based trading platform. 2003: NYMEX launches ClearPort as a response to ICE, and a lawsuit between ICE and NYMEX over NYMEX settlement data ensues. ClearPort clearing frees credit restrictions. 2003: Several amendments in the Commodity and Futures Trading Commission regulations are made that open up the OTC market to NYMEX trading firms, which previously made markets for NYMEX floor-traded products. With these changes, OTC products traditionally traded on a bilateral basis with banks and large energy firms could be offered to customers directly. Any trading firm that meets a minimum capital requirement ($10 million) can trade any NYMEX cleared

30


●

●

product with its customers, provided they have a clearing account with a NYMEX clearing member. 2005: CME announces plans to list ethanol futures contracts, the exchange’s first ever electronic energy contract. 2006: NYMEX and CME offer side-by-side trading of floortraded and electronic futures contracts. ICE launches competitive WTI crude oil futures contract that competes with NYMEX.

CONCLUSIONS OTC and floor-traded derivatives have blended into one. A fund will just as easily trade NYMEX futures as an OTC crack spread. There is less of a line between exchange traded and OTC, especially since the NYMEX has listed OTC contracts formally on the exchange. ICE gives the ability to trade OTC with plain vanilla futures and options, adding to flexibility and transparency. Only the most obscure commodities remain truly OTC. Today, hedge funds operate as market makers, replacing the banks in that role. The fundamentals of energy supply/demand, cost of carry, and relative prices on grades and locations now mean less. In twenty-first-century energy markets, capital flow is driving market price more and more, making short-term price movements increasingly volatile. In fact, we cannot guarantee that prices will be high or low at any given point in time, but we are guaranteed higher volatility due to the increased investment flow in the energy market place. Electronic systems could eventually replace open outcry, but the market remains very much a personal business. Personal relationships are very meaningful, especially in OTC. OTC still remains a closed market of insiders, though it is slowly growing out of such a mentality. NOTE 1. Calendar is the average price over the 12 months January–December. Summer is the average of the seven months April–October. Winter is the average of the five months November–March.

C H A P T E R

4

Energy Derivatives Structures Steve Leppard

RISK AND RISK MANAGEMENT THEMES When I ask most people, even those who work in risk management, trading, and origination, how they define risk, they usually say “randomness,” “uncertainty,” or give a definition related to the negative impact of uncertainty. Financial theory teaches us that without risk, there cannot be some form of return, and since return on investment is a good thing, these negative definitions do not suffice. Furthermore, randomness alone cannot be risk unless one is somehow exposed to this uncertainty, and so I prefer the definition of risk as “exposure to an uncertainty.” Risk takes many forms, and a search of the Web will turn up various versions of the “galaxy of risks,” which purports to enumerate and define every form of risk of relevance to a market participant. The range of risks relevant in the energy sector encompasses all the risks found in the financial domain—including market risk, operational risk, credit default risk, and funding liquidity risk—as well as some specific to the physical energy markets—including technical risk, in which a supply of energy may temporarily disappear due to technical failure of equipment, leaving inappropriate paper hedges in place, or index specification risk, in which the price index on which a contract has been written stops being reported by the standard industry price publication services, and the contracts may not specify a procedure to deal with this eventuality. 31

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In this chapter I will primarily be concerned with commodity market price risk—for discussion of the broader galaxy of risks, see Leppard (2005). Traded markets have emerged to deal with many forms of risk, and various financial instruments or derivatives have been created to allow the transfer of these risks between market players. Energy derivatives are used to transfer the market price risk components of energy transactions or portfolios to various ends: ●

●

●

strategic or short-term hedging of a portfolio of physical and/or financial agreements, to achieve a budget or exploit a favorable market rate; to stabilize cash flows arising from physical production to enable financing; to stabilize cash flows to return a predictable income stream to investors;

and a host of other possibilities. When working with senior management, I prefer to concentrate less on the various derivatives instruments available than on the risk management “themes” these instruments may be used to achieve, including locking in prices, tying commodity costs to revenues, eliminating downside, reducing the cost of protection by sharing upside, matching currency exposures between costs and revenues, delaying a locking-in decision, giving away flexibility in order to reduce the cost of protection, volumetric flexibility, and so on. Derivatives instruments may be combined into “packages” in various ways to achieve these aims, and concentrating on the derivatives at the expense of the themes puts management in danger of concentrating too quickly on the detail before the risk management strategy itself is even understood or defined. In the remainder of this chapter, I ● ●

●

●

consider market risk exposures; survey a sample of energy derivatives instruments, showing what themes they can achieve on their own; look at a few examples of how these derivatives may be combined into packages in accordance with further themes; and finally, review a couple of structures, which may consist of derivatives packages bundled with physical supply or off-take agreements, financing, credit mitigation, and so on.

CHAPTER 4 Energy Derivatives Structures

33

MARKET RISK EXPOSURE Energy players buying and selling at market prices will be exposed to various commodity “underlyings.” Examples are oil producers selling their production at the dated Brent price, large industrial users of natural gas exposed to a price referenced to US Henry Hub or UK National Balancing Point prices, Japanese utilities buying their liquefied natural gas (LNG) at a price indexed to the Japan Custom-Cleared Crude (JCC) price survey, and large airlines exposed to a variety of regional jet fuel prices. A player’s aggregated exposure to the various commodities through time is known as the company’s commodity market “position,” and where a traded forward market exists, it is possible to hold the various supply and off-take agreements, hedges, and so on up against this forward market to understand the cost or benefit of liquidating positions and to benchmark how unhedged competitors may be faring in the market. This process of “marking to market” is fundamental in assessing market price risk and assessing the cost of purchasing protection. Note that not all underlyings have liquid forward markets, and marking to illiquid markets is a non-trivial exercise that one must discuss with one’s auditors. Some market participants may be prepared to make markets in illiquid underlyings (e.g. the JCC), and there is, of course, a cost associated with this service. Risk managers must take advice from their accountants and decide the extent to which they may be exposed to proxy risk (the risk of hedges not perfectly matching the underling exposure). In discussing derivatives instruments it is useful to employ various sorts of diagram. The first of these I will use is the well-known “profit and loss diagram,” which shows how a company’s commodity positions and hedges respond to various levels of the underlying markets. We start by considering a producer of physical energy that is selling its production into the market at some market price index; if the underlying commodity is worth $1 per unit volume, then the producer will earn $1 per unit volume of production; if the underlying is worth $2, the producer will earn $2, and so on. The market risk exposure for this producer is shown in Figure 4.1. The producer’s situation may be summarized in a second form of diagram known as a “transaction diagram” or “block diagram,” which shows the entities involved in a trade and the flows of commodity or cash between these entities. We can now see in Figure 4.2 the producer selling physical volumes (denoted by thick arrows) into the spot market in exchange for the prevailing market price (denoted by a thin broken arrow).

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F I G U R E

4.1

Producer market risk exposure

Source: BP

The situation for a consumer buying at the spot market rate may also be summarized in similar diagrams. The transaction diagram for a consumer buying from the market or a supplier at the floating market index is shown in Figure 4.3, while the corresponding P&L diagram showing the expenditure of cash at this index is given in Figure 4.4. I find that many practitioners lack consistency in the way they distinguish upward and downward price movements from “upside” and “downside” price movements. I define an upside price movement to be one that benefits a market participant, while a downside price movement is one that causes some pain. For a producer, increasing prices are to their

F I G U R E

4.2

Producer transaction diagram

Source: BP


F I G U R E

35

4.3

Consumer transaction diagram

Source: BP

upside, while for a consumer, price increases are to their downside. The converse case for decreasing price movements should now be clear. This distinction between upward and upside and between downward and downside will be used freely in the following section on derivatives instruments. BASIC DERIVATIVES INSTRUMENTS An energy derivatives instrument is here defined to be a contract whose payoff is based upon underlying energy market prices and may involve F I G U R E

4.4

Consumer P&L diagram

Source: BP

36


exchanges only of cash (a paper instrument) or of cash for commodity (a physical instrument). Their principal use is in hedging a physical position, and I shall be concentrating on this hedging activity in this chapter. (Some investors take speculative positions in derivatives in the hope of making an absolute return.) Hedging is a form of trading intended to reduce portfolio variance or stabilize cash flows, and as such, it is not necessarily a way to lock in profits. The stability it provides may, however, be necessary for planning or financing purposes. The simplest form of derivative considered here is the “fixedfor-floating swap,” which provides a regular (assumed monthly) series of cash flows derived from the difference between the average price of commodity in the month just passed and some agreed “strike” price. The transaction diagram for a fixed-for-floating swap is shown in Figure 4.5, where the swap buyer or long is the party receiving the floating payments. Note that the fixed payments, which do not depend on variable market prices, are denoted by a solid arrow. In the example above the floating price exceeds the fixed price payment that the swaps buyer receives, and vice versa. The P&L diagram for a swap (with a fixed price of $50/vol) is shown in Figure 4.6. The role of swaps is best understood when the swap transaction or P&L diagrams are combined with the market exposure of the energy market player. A consumer of physical commodity paying the floating market price under the terms of their supply contract may choose to buy F I G U R E

4.5

Fixed-for-floating swap transaction diagram

Source: BP


F I G U R E

37

4.6

Fixed-for-floating swap P&L diagram

Source: BP

a swap, as shown in Figure 4.7. To understand the effect of the swap, look at the net flows of commodity and cash in and out of the consumer box. The floating payments are assumed to cancel, and in net terms the consumer receives physical commodity in exchange for a fixed payment. This “locking-in” behavior may be understood in quantitative terms from the P&L diagram in Figure 4.8. A range of institutions, including bpriskmanager, offer swaps contracts on underlyings for which there is no liquid forward market. These “proxy swaps” may be offered by institutions with sufficient analysis skills to identify the best correlated hedge instruments for which forward markets do exist, to quantify and price in the likely proxy risk in using only liquid hedge instruments, and to manage and warehouse any remaining proxy errors. All forms of swap contract may further be bundled with physical supply or off-take agreements to create fixed-price physical agreements, removing the need for consumers or producers to enter into separate physical and financial arrangements. Simple or “vanilla” options instruments are generically used to protect producers and consumers against downside price movements. The most common instruments traded over the counter are Asian call and put

38


F I G U R E

4.7

Energy consumer buys a swap to remove floating price exposure

Source: BP

F I G U R E

4.8

Consumer hedged with swap P&L diagram

Source: BP


39

options, which (similar to swaps) have payoffs dependent on average realized market prices. A call option (or “cap”) compensates its holder pennyfor-penny should market prices rise above a specified “strike” price, while a put option (or “floor”) compensates its holder in the case of market prices below the strike. Call options are used by consumers to protect their downside (increasing market prices), and put options are similarly used by producers, leading to the mnemonic “Consumers need Calls, Producers need Puts.” Since options provide a one-sided or asymmetric form of protection, allowing their buyer protection to the downside without requiring payment to the upside, a premium must be due for this insurance-like protection. A generic option transaction is shown in Figure 4.9, where the premium is represented with a solid line (because it is a known amount, usually paid up front), and the option payoff is represented by the broken arrow (since the size of the payment is dependent upon average market prices) overlaid with a circle to indicate that the payment is only made under certain circumstances, where the strike price is passed. The P&L diagram for a consumer hedging with an Asian call option is shown in Figure 4.10. Note that the call option payoff is shifted below the horizontal axis since the premium must be paid irrespective of whether the option is subsequently exercised or not. The corresponding P&L diagram for a producer hedging with an Asian put is shown in Figure 4.11. F I G U R E

4.9

Vanilla option transaction diagram

Source: BP

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F I G U R E

4.10

Consumer hedging with call option

Source: BP

F I G U R E

4.11

Producer hedging with put option


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Options on swaps, swap options or swaptions, are an important building block for many derivatives structures; these derivatives instruments provide their holder with the right but not the obligation to enter into a long or short swap position at expiry, with its usual stream of fixedfor-floating payments, rather than a single cash settlement at expiry. Using similar notation to that employed above, a “call swaption” transaction is shown in Figure 4.12; a call swaption is an option to go long a swap (i.e. to receive floating), while a put swaption is an option to go short a swap (i.e. to receive fixed). On exercise of a call swaption the swaption buyer will be long a swap, with the diagram shown in Figure 4.5. Swaptions may be used to achieve the theme of “delaying a locking-in decision”; buyers of swaptions often want the right, but not the obligation, to lock in market prices at the current rate. Market players often enter into swaption positions for their ability to extend or cancel the term of an underlying swap deal, as shown in the next section on derivatives packages. Many other derivatives instruments will be employed in addressing energy players’ risk exposures. The following are some examples: ●

“Digital options” pay a fixed lump sum if market prices move beyond a certain threshold. A digital call option pays a lump sum if market prices move above the threshold strike price, while a digital put pays a lump sum if market prices move below the strike.

F I G U R E

4.12

Call swaption transaction diagram

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●

●

Refinery “basket options” are options where the payoff is based not upon a single underlying commodity, but on a weighted average of different commodity prices. Basket put options may be used by refiners to protect their refinery crack. All the above instruments, and more, can have their payoffs in either the native currency of the underlying commodity or in “quanto” form. Quanto derivatives have their payoffs translated into some other currency before settlement is made in that currency.

DERIVATIVES PACKAGES A derivatives “package” is a collection of two or more derivatives such as swaps, caps, floors, baskets, or digitals. Packages are usually used ●

●

to give a more tailored form of risk mitigation for a company’s exposure; as a way of reducing the cost of protection by giving up some upside.

Rather than attempt a methodical exposition of packages and their construction—see Leppard (2005)—a few examples will be provided here to illustrate the principles. “Floating-for-floating” or “reference” swaps are packages in which a stream of floating payments on one commodity is exchanged for another. This is often used to tie one’s costs to one’s revenues or in exchanging an exposure to an illiquid commodity for a more liquid one to access a wider range of risk management tools. A reference swap may be constructed as a package of one long and one short vanilla fixed-for-floating swaps in such a way that the fixed legs cancel. Consider a consumer of physical commodity who receives physical commodity in exchange for payments based upon a floating reference price 2. They wish to exchange their payments based upon reference 2 for payments based upon reference 1, and so they enter a reference swap with their energy risk manager. The transaction, shown in Figure 4.13, is engineered such that the fixed prices of the two fixed-for-floating swaps cancel; upon removing all flows through the consumer that cancel, the net flows in Figure 4.14 are obtained—this diagram illustrates the motivation behind the reference swap. An example of the use of reference swaps will be given later in this chapter.


F I G U R E

4.13

Reference swap

Source: BP

F I G U R E

4.14

Reference swap: net flows

Source: BP

43

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A “collar” is a derivatives package consisting of a cap and a floor, with one of these positions being long and the other short. A consumer who requires the downside protection offered by their buying a call may feel that the option premium is unaffordably high. To reduce the cost, they may sacrifice some of their upside by selling a put of lower strike, earning them premium. Should market prices rise above the call strike, the consumer will have the absolute downside protection they require; if market prices drop, however, which is to the consumer’s upside, then the collar structure means they will not enjoy the benefit of these decreasing market prices below the put strike. The payoff diagram illustrating these points is shown in Figure 4.15. The act of both buying and selling options means that only the net premium needs to be paid. Collars may be constructed such that the premia of the call and the put cancel each other precisely, leading to a “zero-cost collar.” In computing the payoffs in Figure 4.15 it has been assumed that the collar is a zero-cost structure. Figure 4.16 shows the transaction diagram for a zero-cost collar, with netted premium. Other derivatives packages built from simple calls and puts are “three-way options,” in which the purchaser of the structure may benefit

F I G U R E

4.15

Consumer collar P&L diagram

Source: BP


F I G U R E

45

4.16

Zero-cost collar transaction diagram

Source: BP

from sufficiently large upside movements, and “participation” packages, in which the volumes of calls and puts transacted do not match exactly, leading to a sharing of upside. An “extendible swap” is a package consisting of a vanilla swap and a swaption. The vanilla swap locks prices in for some initial term, while the swaption permits one of the parties to lock in prices for a further “extension period” should the swaption be in the money. Extendible swaps are most often constructed such that the energy risk manager has the extension right, for which they must pay a premium, reducing the fixed leg payments on the initial term. An example of an extendible swap transaction for a consumer is shown in Figure 4.17. Extendible swaps are examples of “term-flexible” structures, in which a derivatives package may be extended, or some portion cancelled, with an adjustment to cost arising from gain or loss of flexibility. Options themselves may be extended, and some energy risk managers are able to offer extendible packages where collars, three-ways, participations, capped swaps, and so on may all have extension or cancellation rights. The fundamental message from package construction is that there is no such thing as a free lunch: all downside protection comes at a cost, and all reductions in the cost of downside protection come at the expense of

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F I G U R E

4.17

Extendible swap: energy risk manager has extension option

Source: BP

giving up some upside or flexibility. While sacrificing upside may appear to be a price worth paying, hedgers need to remember that their unhedged competitors may be enjoying this upside, making them more competitive. STRUCTURING AND DERIVATIVES STRUCTURES Derivatives structuring is the activity of designing and executing risk management structures together with the broad risk analysis, contractual design, and so on necessary to ensure that these structures properly match the clients’ and the company’s risk exposures and appetites. Since BP is an integrated financial and physical player, in bpriskmanager we consider the joint requirements of designing hedgeable derivatives structures matched to our counterparty’s exposures, the demands of physical agreements and risks (including force majeure risk), and interaction with financing structures, all while ensuring these arrangements keep BP’s credit exposure to an acceptable level. In this section, I give a couple of examples of derivatives structuring which draw on the themes and instruments outlined above—for further discussion, see Leppard (2005). The first example we will consider is that of a Japanese importer of LNG paying for their commodity on the basis of the JCC survey of imported crude prices. The importer would like to manage their JCC exposure, but there is no liquid forward market in JCC prices.


F I G U R E

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4.18

LNG importer swapping JCC for WTI exposure

Source: BP

They decide to exchange their yen-denominated JCC exposure for a US-dollar-denominated West Texas Intermediate (WTI) crude exposure, which will permit the use of a wide range of very liquid, and hence cost-effective, derivatives instruments on WTI. The basic reference swap transaction is shown in Figure 4.18. The LNG importer subsequently decides they would like to put a zero-cost collar around their commodity price exposure, which they can now execute on the liquid WTI reference. The complete transaction, with netted payments removed, is shown in Figure 4.19. Consider now the case of an oil major which is disposing of an oilproducing asset and is collecting bids from various players. The two most competitive bids are quite different in nature, and the mergers and acquisitions group does not know how best to compare the values of the two deals. They turn to the structuring group on the trading floor for assistance in understanding the two bids, which are as follows. ● ●

Bidder A offers a fixed lump sum of US$50 million for the asset. Bidder B offers a smaller lump sum of $35 million up front for the asset and lump sums of US$10 million in each of the three following years if oil prices remain above an average of $40/bbl in the respective years.

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F I G U R E

4.19

Reference swap with price risk management on the liquid WTI reference

Source: BP

These bids are represented using the transaction diagrams in Figure 4.20. The structuring group recognize bid B as being equivalent to a lump sum plus a strip of three Asian digital options, each with a one-year averaging period. The M&A group are not interested in receiving marketcontingent payments for an asset they have sold some years before but are required to maximize income for the company. The structuring group offers to monetize these embedded digital options; this amounts to buying the options from the M&A group and offering them a firm up-front premium payment in exchange for the market-contingent future cash flows. The M&A group would therefore receive $35 million plus the premium for the asset, and the structuring group would be buying a strip of Asian digital options. The proposed transaction is shown in the transaction diagram in Figure 4.21. The strategy for the M&A group, who are required to maximize income for the company, is now clear: if the $35 million lump sum plus the option premium exceeds the alternative $50 million in bid A, they should proceed with bid B. The structuring group quotes a premium of $22 million for the strip of digital options, which together with the lump sum of $35 million, comfortably exceeds the $50 million bid. The M&A


F I G U R E

4.20

Bids for oil-producing asset

Source: BP

F I G U R E

4.21

Monetization of embedded digital options

Source: BP

49

50


group proceed with bid 2, raising $7 million of additional value from the asset disposal. REFERENCE Leppard, S. (2005) Energy Risk Management: A Non-technical Introduction to Energy Derivatives (London: Risk Books).

C H A P T E R

5

The Nordic Electricity Markets Per Christer Lund, Åsmund Drivenes, Bjørn Tjomsland, and Per Otto Larsen

INTRODUCTION There are several regulated energy futures exchanges. The major ones are the New York Mercantile Exchange and the International Petroleum Exchange, which are global oil futures exchanges that also offer regional natural gas contracts. The third most successful energy exchange is the Nordic Electricity (Nord Pool), which is the oldest and most successful electricity futures exchange in the world. Other energy futures exchanges are the Tokyo Commodity Exchange (Tocom), the Singapore Exchange, the Shanghai Futures Exchange, and various European electricity exchanges that have varying degrees of liquidity. This chapter focuses on the development and success of the Nordic markets. THE NORDIC ELECTRICITY MARKET The Nordic electricity market is today the most open electricity market in the world and consists of Norway, Sweden, Finland, and Denmark. All customers have access to a competitive market, and retail wheeling has been implemented to cover all customer groups, including small households. The organized Nordic market has evolved gradually, from the introduction of Norway’s Energy Act in 1991 to the integration of eastern Denmark in Nord Pool on October 1, 2000. In 2004, close to 400 TWh 51

52


were generated in the four countries, supporting a population of about 24 million. The installed capacity at the end of 2004 was at 92,000 MW. Figure 5.1 shows the configuration of the market. The fuel mix in the Nordic energy market is geographically skewed, with close to 100% hydrogeneration in Norway and northern Sweden and

F I G U R E

5.1

Nord Pool power market

CHAPTER 5 The Nordic Electricity Markets

F I G U R E

53

5.2

Total fuel mix in the Nordic energy market

Source: Nord Pool

thermal production, including nuclear power production, in the southern and eastern parts of Scandinavia. The pie chart in Figure 5.2 illustrates the total fuel mix. The Nordic electricity market was one of the very first to deregulate and has gradually grown to be among the largest deregulated electricity markets in the world, both with respect to total generation and installed capacity. Figure 5.3 places the Nordic market among a representative selection of the leading deregulated electricity markets worldwide.

F I G U R E

5.3

Comparison of power generation

Source: Nord Pool

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The market has the following characteristic features, developed over the 14 years the market has been operational. ●

●

●

●

●

●

Postage-stamp-like transmission tariff system based on nodal pricing. Abolition of border tariffs between the countries. Transmission tariffs are completely independent of trading agreements. The Nordic electricity exchange, Nord Pool, organizes a day-ahead market (Elspot) and a financial settled futures market, where it is possible to trade a number of different contracts up to four years ahead. Dispatching of the system is based on commercial bids both from sellers and buyers of electricity in the market. Also in short-term operation of the network the system operators are obliged to use market operations as far as possible. Each of the national transmission system operators (TSOs) uses balancing markets and procured ancillary service to balance the systems. There is some exchange of these services between the TSOs. All market participants are free to negotiate bilateral physical contracts. But trade in the futures market is increasing rapidly. In Norway a majority of long-term contracts are now financial, with physical electricity being traded in the spot market. Prices in all markets, including bilateral contracts and the retail market, relate to the spot market and are to a great extent reflecting changes in supply and demand. Ownership and stakeholder relations

Nord Pool Spot AS is owned by the Nordic TSOs (Statnett, Svenska Kraftnät, Fingrid, and Energinet.dk), with 20% each. The remaining 20% is owned by Nord Pool ASA. The TSOs supply Nord Pool with production, consumption, and import/export data for Norway, Sweden, Finland, and Denmark. The TSOs are obliged to disclose national transmission grid and inter-country interconnection availability and status to Nord Pool Spot. Nord Pool Spot participants are obliged to disclose physical and operational availability and status for generating units and load nodes. Although Elspot is not involved in unit commitment or unit scheduling, the market operator


55

requires this information for transmission congestion management purposes—see below—and for market surveillance purposes. HISTORY AND DEVELOPMENT OF THE NORDIC ELECTRICITY MARKET The process of developing the Nordic market was founded on a tradition of cooperation through the former cooperative body Nordel, as seen in Figure 5.4. Initially, the large national power companies in the Nordic region were considered too dominant to operate in competitive national markets. In a common Nordic market these companies’ relative sizes decrease, and at the same time they will be large enough to meet the emerging competitive European market. This was one of the main objectives to forming a common Nordic market. Deregulation processes of power sectors are often closely connected to privatization of the industry. This is not the case in the Nordic area. The state and public ownership of most of the power industry has been maintained in the new competitive environment. The structure of Nordic electricity consists of a competitive power generation sector, TSO monopolies in each country for management of the high-voltage transmission grid, a large number of monopoly network owners at the local and regional level, and finally, a common Nordic Power Exchange. Within the common power market the market participants from both the demand and supply sides trade bilaterally and on the power exchange on equal terms. Regarding transmission, the participants face a pointof-connection tariff, as if the Nordic grid was merged into one grid only. F I G U R E

5.4

Development of the Nordic power market

Source: Nord Pool

56


Despite national dissimilarities, the market has developed into a well-functioning competitive power market. The most important agencies of the restructured Nordic power market are the regulators, the grid owners, the transmission system operators, and the power exchange. The roles and responsibilities of these key players in the power business differ between the Nordic countries due to tradition, power system structure, and regulatory framework. There is an ongoing harmonization process in the area to develop a common solution between the TSOs, and this process is supported by the Nordic Ministers of Energy. NORD POOL SPOT: THE PHYSICAL DAY-AHEAD MARKET Nord Pool Spot, the leading power exchange in Europe, organizes and operates a marketplace for physically binding day-ahead electricity contracts in the Nordic countries. Elspot is a voluntary, auction-based day-ahead market covering all the physically interconnected Nordic countries. Elspot trades portfolio contracts, that is, contracts for purchase and sale of energy, and is not involved in any kind of unit scheduling. The concept of “balance responsibility” adopted for all market participants transfers the task of unit commitment and scheduling to the individual market participants. In this respect, the Nordic electricity market differs significantly from, for example, US “standard” market designs, which are largely based on the concept of centralized dispatch. In addition to Elspot, Nord Pool Finland Oy, a fully owned subsidiary of Nord Pool Spot AS, operates the “Elbas” market, which is a continuous intra-day physical power market for Sweden, Finland, and eastern Denmark. DETERMINATION OF DAY-AHEAD MARKET CLEARING PRICES The day-ahead market concept is based on bids for purchase and sale of power contracts of one-hour duration that cover all 24 hours of the next day. Three bidding types are available: hourly bids, block bids, and flexible hourly bids. After gate closure at 12 noon, the individual buy bids and sell offers are aggregated into an aggregate demand curve and an aggregate supply curve for each power-delivery hour, as illustrated in Figure 5.5. The day-ahead market-clearing price for each hour is determined by the intersection of the aggregate supply and demand curves. This unconstrained day-ahead market price is called the system price.


F I G U R E

57

5.5

Aggregate supply/demand curves for pricing

Source: Nord Pool

The system price is the reference price for the Nordic financial power market as well as for the bilateral wholesale power market covering this region. During 2004, about 167 TWh, which represents over 42% of Nordic consumption, was traded in Elspot. Transmission Grid Congestion Management The Elspot market is also the primary Nordic marketplace for day-ahead congestion management, that is, insufficient transmission capacity between segments of the grid. This implies that Elspot is a marketplace where energy and capacity are combined into one simultaneous auction. AREA PRICES AND MARKET CHARACTERISTICS The Nordic market is divided into predefined bid areas. The participants must specify the area to which each bid relates. The TSOs define the interconnection transmission capacity between each bid area. If the total contractual flow calculated at the unconstrained system price exceeds a given interconnection capacity limit, the market will be split along that interconnector, and new area prices will be derived for each of the separated areas. The split represents the boundary between

58


a surplus area and a deficit area, where the flow between them is limited by the interconnector capacity. Since demand and supply are pricedependent, the area price in the surplus area is reduced and the area price in the deficit area is increased until the energy flow between the areas equals the interconnection capacity limit. The Elspot market concept for transmission congestion handling thus guarantees the optimal utilization of interconnector capacities through this implicit transmission capacity rights method. Note that this is conceptually different from the explicit firm transmission rights auction concept adopted in, for example, US markets. The Nord Pool Spots Intra-Day Market Elbas The time span between the day’s Elspot price-fixing and the actual delivery hour of the contracts concluded is quite long (36 hours at the most). As consumption and production situations change, a market player may find a need for trading during these 36 hours to adjust its exposed positions. The Elbas market offers continuous power trading 24 hours a day, 7 days a week, covering individual hours, up to one hour prior to delivery. The traded products are firm power contracts of one-hour duration. The participants are power producers, distributors, industries, and brokers in Finland, Sweden, and eastern Denmark. Figure 5.6 shows the dynamics of the real-time market.

F I G U R E

5.6

TSO: real-time market

Source: Nord Pool


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SETTLEMENT Nord Pool Spot AS acts as counterpart in all contracts traded on the Elspot market. All trades are physically settled with respective TSOs. Monetary settlement is an automatic process where the Elspot participants are connected to the settlement system through a bank account structure of several co-operating depository banks. Invoices and credit notes of a delivery day’s net purchases/net sales are sent by email the following banking day. Net purchases are automatically debited one settlement day after the delivery day, and net sales are automatically credited three days after the delivery day. The Financial Market: Nord Pool Financial Market and Clearing Services Nord Pool ASA organizes exchange and clearing operations for financial derivatives to facilitate trade in electricity contracts. All financial contracts listed at the exchange are based on the daily system price from Nord Pool Spot’s day-ahead market. Risks in the Electricity Market The deregulated Nordic electricity market—including Norway, Sweden, Finland, and both eastern and western Denmark—has posed new challenges for power companies. Traditional views, based on secure delivery of electricity and a macroeconomic perspective, have been replaced by a market where the focus is based on a more economic, rational exploitation of resources. The integration of the three main business areas—production, distribution, and retailing—is not proven to create value in a deregulated market. A criterion of efficient markets is that integration gives no extra margins or risk-free profits. In order to maximize production and retailing potential, power companies seek to specialize. Enhanced competition is likely to force companies to specialize in any of the three functions— power generation, power distribution, or power retailing. All power companies have in common an increasing need for risk management. The Nord Pool participants range from physical players to pure financial players. The physical players are power generators, power distributors, and power retailers. Power generators produce and sell power to retailers or to the power exchange. Power distributors operate the low-voltage

60


electricity grid. Each distribution company has been given a concession to operate a specific section of the grid. Through this concession they are obliged to distribute all the power requirements needed. Power retailers sell electricity to end users. They acquire electricity either via Nord Pool or on the over-the-counter market. However, even though there has been specialization among the power companies, some of these still operate in more than one business area. The pure financial players are basically international trading houses trading the financial market for profit and thereby adding price depth and liquidity to the market. THE ELECTRICITY DERIVATIVES MARKET: NORD POOL FINANCIAL MARKET The Nord Pool financial market offers derivative contracts related to delivery periods of electricity up to four years ahead. These contracts are short-term futures, medium- and long-term forwards, contracts for difference (CfDs), and options contracts. All derivative contracts traded on the exchange are cash settled and cleared by Nord Pool Clearing. Currently, Nord Pool has eight market makers quoting prices in the financial market. Trades in Nord Pool’s financial market are mainly conducted electronically. Nord Pool offers an electronic order book and an on-line trading facility to which participants have direct access for information purposes and for the recording of orders on their own behalf or on the behalf of clearing customers. Figure 5.7 shows the development of the financial market in over-the-counter bilateral trades and on the exchange since 1996. Electricity derivatives, combined with trade on the physical spot market, are important tools for power companies when managing price risk in the Nordic electricity market. Nord Pool offers different instruments to reduce the exposure to the price risk. These instruments are, as mentioned earlier, spot contracts, forwards, including CfDs, futures, and European options with the forward contracts as underlying. The physical spot contracts are based on day-ahead auction trading and are also the basis for the reference/system price for the financial market. The system price is the market equilibrium between demand and supply for the whole exchange area consisting of Norway, Sweden, Finland, and Denmark. Looking at different commodity markets for energy, the Nordic electricity market is probably the most difficult and volatile. The pricing of individual electricity-contingent claims is a very complex task. In the Nordic electricity market, there are mixed generation systems, where hydropower


F I G U R E

61

5.7

Market development from 1996 to 2005

Source: Nord Pool

is significant; during wet years the electricity is exported, and dry years entail electricity imports. In addition, the varying climate situation and the large seasonal variations make the Nordic market even more difficult to predict than central European electricity markets. The central European markets are based mainly on thermal power production, which makes the electricity price less volatile and more dependent on economic trends. Figure 5.8 shows the development of the Nord Pool Spot system price from 1996 to 2005. It illustrates the high price volatility in the Nordic market. The volatility can, to some extent, be accredited to factors such as non-storability and shortage of interconnector capacity. This results in a significant need for risk management tools for Nordic market players. Area Price Risk The financial market is based on the system price in the physical market, which is calculated as the market equilibrium for the whole exchange area without any constraints. Owing to shortage of interconnector capacity, the different areas within the exchange area usually experience prices different from the system price. Therefore, hedging of physical positions through the system price contracts did not provide a 100% correct hedge. As a result, Nord Pool introduced CfDs such that trading the system price contract combined with a CfD provides a correct price hedge in a specific area. The area prices currently possible to trade are southern Norway (Oslo),

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F I G U R E

5.8

Nord Pool system price

Source: Nord Pool

Sweden (Stockholm), Finland (Helsinki), western Denmark (Aarhus), and eastern Denmark (Copenhagen). Liquidity Risk Liquidity risk refers to the risk of not meeting counterparties in trading within reasonable market prices. The presence of market makers and the steady increase in the number of participants and orders per participant have created a market where this type of risk has been reduced to a minimum. Currency Risk In the physical market Nord Pool offers participants the opportunity to trade in different currencies in the exchange area. Currently, it is possible to trade in NOK, SEK, DKK, and euros. In the financial market, trades are conducted in euros. Participants exposed for currency risk will normally also trade currency based on their positions in the electricity market. COUNTERPARTY RISK Through Nord Pool Clearing, clearing services are offered to the participants in the Nordic electricity market. This makes Nord Pool the contractual


63

counterpart that guarantees correct settlement. As all contracts are cleared automatically, including trades reported in for clearing from the overthe-counter market, participants can pass on default risks to Nord Pool. Administrative Risk Nord Pool sends reports to different levels in the participants’ organizations on a daily, weekly, and monthly basis to show the companies’ exposure. The reports give an overview of net positions, profit/loss, etc. Other types of aggregated risk on a portfolio level can be managed through the use of different models, such as value at risk and profit at risk. Systems handling this kind of risk can be found as standard software packages from system vendors specializing in the electricity market. However, the major players usually develop their own systems using their competence from other commodity markets. Nord Pool is offering services and different products in both the physical and financial markets that can be used to offset or partially offset the risks mentioned above. THE EMISSIONS MARKET: NORD POOL CARBON DIOXIDE ALLOWANCE MARKET Nord Pool is the leading marketplace for trading and clearing European Union carbon allowances (EUAs), with the largest and the most geographically and industrially diverse customer base. It intends to grow and retain its present position by providing an exchange which represents the complete market, with its varying views on the value of EUAs. Prices have moved tremendously and the volatility has been high since the opening of the market in EUAs. The prices were up to E30 per tonne of carbon by July 2005 but had stabilized somewhat around E22 by the end of August 2005 (see Figure 5.9). Power prices are undoubtedly higher than they would have been without EUAs. That was also an intended outcome. But the EUA market is still young, and it remains to be seen what the price range will be and how much of the increase will be passed on to the end user through the cost of power. Nord Pool was the first exchange in the world to offer trading and clearing of EUAs. It is thereby facilitating more efficient trading of these instruments across national boundaries and industrial sectors. Efficient trading is important if the EU is to succeed with its model for meeting the obligations imposed under the Kyoto protocol. Although

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F I G U R E

5.9

EUA price development from February to August 2005 (E/tonne CO2)

Source: Nord Pool

opening a marketplace for EUAs was a business concept at Nord Pool rather than a political issue, this undoubtedly poses some political challenges. By ratifying the Kyoto protocol, Europe has taken on major costs in reducing carbon dioxide emissions. The higher cost to industry and households from the EUAs in a Europe that emits 30% of the world’s carbon dioxide needs to be matched in some way by the 70% of the world that has not ratified the agreement. The result will otherwise be a shift in industrial location rather than a reduction in emissions. A deficit in EUAs has been built into the market. That might make it seem as if there should only be buyers of EUAs. With its background in the Nordic power market, however, Nord Pool has experienced the market dynamics of EUAs at work. The Nordic market involves a mix of electricity generation technologies, with a substantial element of hydropower. That provides the dynamics of EUA owners who could make money by selling allowances instead of generating power from carbon-related sources during mild, wet years. Similar dynamics are designed into the EUA market. Countries have been allocated different percentages of the “normal” demand for carbon emissions, meaning some countries are less short or perhaps more long than other countries within the scheme. That should make for a difference


65

in value of the EUA in different countries or geographic regions. This makes it vital for Nord Pool to have a geographically spread customer base to get liquidity going. Further, correlation in demand is low in different industries in the current allowances scheme. Should Europe experience extremes of temperature, demand for EUAs is likely to be high from carbon-emitting energy generators. But that in itself should not affect the demand for cement, for example, which is another industry in the current scheme. Having present members from all relevant industries trading at Nord Pool is another priority. Nord Pool already represents this inherent market dynamic by having geographically spread members and various industries. The membership and price structures are also designed to suit diverse needs that arise from diverse customer needs. CONCLUSIONS The Nordic electricity market is centered around the day-ahead market and Nord Pool Spot and Nord Pool financial markets. Over the last 14 years it has grown into one of the largest and arguably the most successful electricity markets in the world. Its success can be assessed both quantitatively through the large and increasing liquidity on both the physical and financial sides and qualitatively on the market participant satisfaction side, the very low occurrences of complaints and market abuse, or attempts at such. Nord Pool is frequently referred to as a pragmatic market model, leaving the main responsibility for economic dispatch to the market participants as opposed to the centrally dispatched markets popular in such countries as the US, Canada, and Australia. The pricing algorithm and congestion management concepts used in Nord Pool are appreciated by the market participants as much simpler and easier understood mechanisms than complex schemes for security constraint unit commitment combined with implicit transmission, capacity, and reserve calculations. The Nordic market concept and the Nord Pool model have, over the last decade, attracted significant attention among authorities in deregulating electricity markets, and Nord Pool, through its consulting arm Nord Pool Consulting AS, is or has been involved in the design of a number of electricity markets worldwide, including France, Germany, Romania, Bulgaria, Turkey, India, Japan, and the South Africa Power Pool.


C H A P T E R

6

Market Risk Measurement and Management for Energy Firms José Ramón Aragonés, Carlos Blanco, Kevin Dowd, and Robert Mark

INTRODUCTION This chapter describes best practices in the measurement and management of market risk. The importance of this topic is self-evident: energy markets are extremely volatile, and energy firms frequently get into serious financial difficulties. It is also very clear that firms that do not implement best (or at least tolerably good) practices are much more likely to experience major financial problems. Being aware of best practices and striving to implement them are therefore keys not just to success, but to having good prospects of longer-term survival. In discussing best practices, we also take the opportunity to explore recent methodological advances, such as the development of coherent risk measures and methods of modeling mean reversion and jumps in energy price processes. We would also emphasize the importance of stress testing and scenario analyses to complement probabilistic risk measurement exercises. This chapter is organized as follows. We start with an overview of the main market risk measures as well as the main estimation approaches (Monte Carlo simulation, historical simulation, and parametric methods). In the next section we show how to estimate market risk measures for energy derivatives using a mean-reverting jump-diffusion process to capture some of the main features of energy price behavior. We next focus on alternative approaches to modeling energy price spreads, one of the 67

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key issues for energy firms. The following section covers stress tests and scenario analysis in an energy context, and we conclude with an overview of general risk management issues for energy firms. MEASURE OF MARKET RISK Types of Risk Measure The first task is to select the most appropriate market risk measure. The most popular risk measure is, of course, the value at risk (VaR) measure, which is essentially just a quantile on a loss distribution.1 VaR is a standard risk management tool that provides a quantification of the primary risk exposures that a firm faces. It also offers crucial information on the overall risk profile of the firm to senior management, traders, shareholders, auditors, rating agencies, and regulators. Once the exposures to several risk factors have been identified and quantified, it is possible to analyze how those risk exposures interact with each other and to determine which ones are acting as natural hedges to the portfolio and which ones represent the largest sources of risk for the firm. VaR measures can be also used to assist a firm to minimize the variability of the firm’s earnings, decide which risks are worth taking, and hedge those that may cause “too much” earnings variability. One of the main advantages of VaR is that it constitutes a userfriendly way to present concise reports to the board of directors and comply with regulatory requirements. However, VaR has well-known inadequacies (see Box 6.1). Coherent risk measures have theoretically superior properties (especially subadditivity) and avoid many of the problems that can arise when VaR is used as a risk measure. Coherent risk measures are weighted averages of the quantiles of the loss distribution, and can therefore be regarded as weighted averages of VaRs. The best known of these is the expected shortfall (ES), which is the average of the worst 100ρ% of losses, where ρ is the probability of a tail loss. There are many other coherent risk measures; particularly promising are spectral risk measures that take weighted averages of losses based on the user’s risk aversion. So, for example, if two energy traders with different degrees of risk aversion have the same portfolio and estimate spectral risk measures for it, then the more risk-averse trader should produce a higher estimate for the risk measure, and this higher estimate would reflect his or her greater risk aversion.

CHAPTER 6 Market Risk Measurement and Management for Energy Firms

B O X

69

6.1

Is VaR a “good” risk measure? Despite its widespread use, VaR suffers from serious limitations as a risk measure. The most serious limitation is that VaR only tells us the most we can lose if a tail event does not occur (e.g. it tells us the most we can lose 95% of the time); if a tail event does occur, we can expect to lose more than the VaR, but the VaR itself gives us no indication of how much that might be. Hence two positions can have the same VaR—and therefore appear to have the same risk—and yet have very different risk exposures. The fact that VaR ignores the values of losses in the tail also implies that the VaR has the (highly) undesirable property of not being subadditive.∗ It also means that the VaR can lead to unsatisfactory outcomes when used for risk–return decision-making,† particularly with decentralized decisionmaking. The classic example is where energy traders facing VaR-defined risk targets “spike” their firms by selling out-of-the-money options that lead to higher income in most states of the world and the occasional large hit. If the options are suitably chosen, the bad outcomes will have probabilities low enough to ensure that there is no effect on the VaR, and the trader benefits from the higher income (and hence higher bonuses) earned in “normal” times when the options expire worthless. Thus the use of a VaR risk measure can encourage traders to promote their own interests at the expense of those of their employers. ∗Subadditivity means that the risk of two positions added together is never greater than sum of the risks of the two individual positions. Subadditivity is a desirable property in a risk measure because we typically expect risks to diversify, and certainly not increase, when we put them together. †For

example, if we use VaR as our risk measure in a risk–return framework, we can get situations where we always prefer to take on more risk at the margin, regardless of the risk and expected return involved, provided only that the risk does not exceed a VaR threshold (i.e. so there is no substitutability between risk and expected return). This is obviously highly undesirable in a risk–return analysis

Estimating Risk Measures Having decided which risk measures to estimate, we then choose a suitable estimation method. To decide on this, it is common to think in terms of the classic VaR trinity: ●

Non-parametric methods. These methods estimate risks using a historical simulation data set, making minimal assumptions about the loss distribution. These approaches do not require strong assumptions about underlying distributions, the distribution of profits and losses or returns. The most popular non-parametric approach is historical simulation, in which a number of hypothetical portfolio profit and loss (or return) observations are simulated based on historical risk factor returns. The portfolio profit and loss observations are then ordered from

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●

●

lowest to highest, and the VaR is obtained as the relevant order statistic (i.e. kth largest observation) from the historical distribution. Parametric methods. These methods are based on assumptions about the particular form of the loss distribution (e.g. we might assume that losses are normal, lognormal, etc.). We then “fit” the relevant curve and use a formula to obtain the VaR. Parametric approaches specify that the loss distribution takes some particular form (which might be Gaussian, t, Lévy, etc.). We then estimate the parameters of the underlying distribution using observed data and an estimation method suitable for the distribution we are dealing with. The VaR can then be obtained using the quantile equation appropriate for the distribution we have chosen, using the parameter estimates we have just obtained. Monte Carlo simulation methods. We specify the stochastic process or processes, calibrate the parameters, and then run a sufficiently large number of simulation trials. Each of these trials gives us a simulated loss value, and we obtain our risk estimates from the distribution of simulated losses. These require that we identify the underlying risk factors in our portfolio, make assumptions about their distributions, and then specify how the market values of the instruments in our portfolio depend on those risk factor changes. We then construct hypothetical paths for the value of our portfolio by drawing a set of random values of the stochastic variables that will determine the price of the instruments within the portfolio. Monte Carlo simulation enables the calculation of risk measures for quite complex portfolios, including those with non-linear risk factors or path-dependent instruments.

Each of these methods has advantages and disadvantages. Nonparametric methods have the advantage of being robust, but their main disadvantage is that estimated risk measures are dependent on the implicit assumption that the future will be similar to the recent past. Parametric methods are more powerful but are dependent on the parametric assumptions and are limited to fairly simple problems. However, Monte Carlo methods can be applied to a wide range of problems and are very good at dealing with complicating factors—such as “badly behaved” stochastic


71

processes (see the next section), heavy tails, path dependency, non-linearity, and optionality—that often defeat other approaches. Their (fairly minor) disadvantages are that they are less easy to use than some alternatives, require a lot of calculations, and can have difficulty with early-exercise features. In short, Monte Carlo methods have major advantages and minor disadvantages and will often be the method of choice for many of the problems we are likely to encounter in practice—especially problems with a series of complicating factors.

ESTIMATING MEASURES OF MARKET RISK WITH A MEAN-REVERTING JUMP-DIFFUSION PROCESS Let us assume that we have long and short positions indexed to spot electricity prices, and we wish to use Monte Carlo to estimate the 95% VaR and ES over a horizon of, say, 10 trading days. We need to choose a suitable stochastic process for the underlying energy price, and this requires that we establish the stylized facts of the empirical process(es) we are trying to model. We can empirically observe that electricity price processes are typically characterized by diffusion, strong mean reversion, and occasional sharp spikes. Such a process is considerably more complicated than the geometric Brownian motion (or basic diffusion) process often assumed for derivatives pricing and market risk measurement. Instead, what we need is a mean-reverting jump-diffusion model,2 and the extra complexities involved mean that estimating a VaR or ES becomes almost impossible unless we resort to Monte Carlo simulation. One of the best processes that has these features is the (fearsome!) arithmetic Ornstein–Uhlenbeck process with jumps: dx = ηE ( x − x )dt + σ E dz E + dqE ,

(6.1)

where x is the natural log of the spot energy price E, x is the (log) long-run equilibrium price to which the (log) spot price x tends to revert, ηE > 0 indicates the mean-reversion rate, σE is the volatility (rate) of the diffusion process, dzE = εE (dt)0.5 is a Wiener diffusion process with a standard normal random variable εE, and dq is a Poisson process taking the value 0 with probability (1−λE)dt and the value φE with probability λE dt.3 Variable λE, in turn, is the frequency rate with which jumps

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occur, and we assume for simplicity that the random jump factor, φE, is normal with mean kE and volatility δE. The first term on the right-hand side of (6.1) provides for mean reversion: it shows that prices have a tendency to rise if x < x and to fall if x > x . The second term is the random diffusion component, which provides for prices to disperse over time, and the third term gives us our occasional random jumps. We then take an exact discretization of (6.1) for simulation purposes. Using obvious notation, our simulation equation applied to daily data is xt = xt −1e

− ηE

+ x ⎡⎣1 − e

− ηE

(

)

⎤ + σ E ⎡1 − e −2 ηE ⎤ 2ηE N ( 0,1) + jumps , (6.2) ⎣ ⎦ ⎦

where N(0,1) is a random drawing from a standard normal distribution, and jumps is the random jump process. The spot price is then 2 ⎡1 − e −2ηEl ⎤ σ E σ ( x ) = i Et = exp[xt – 0.5σ(xt)], where ⎣⎢ ⎦⎥ 2ηE

(6.3)

We now calibrate the parameters and use (6.2) and (6.3) to simulate the energy price. A simulated price path is shown in Figure 6.1, based on a plausible set of parameter values.4 This simulated process exhibits the occasional spikes and strong mean reversion characteristics of real energy market prices, and the simulated prices—with a mean of around $30 per kilowatt-hour and short sharp spikes hitting $200 or more—are empirically plausible. Note too that these spikes create the potential for large gains on the long position and corresponding large losses on the short position. A summary of risk measures for a simulation with 10,000 random paths is shown in Table 6.1. It is striking to see that the VaR and ES for the short position are over 10 times larger than those for the long position. This large asymmetry between the long and the short risk measures reflects the fact that the spikes are good for the long position and bad for the short one. The importance of the jumps is reinforced further by estimates of the risk measures we would get if there was no jump process at all: in this case, the estimated VaRs and ESs are quite small and relatively symmetric across long and short positions. Ignoring the jumps can therefore make a big difference to estimated risk measures, especially estimates of short-position risk measures; in other words, it is important to get the jumps “right.”


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6.1

A simulated energy price process. Simulated with parameter values using (6.1)–(6.3) with 0.5, and λE = 10

E0 = E = 30 , ηE = 30, σE = δE kE =

Source: Black Swan Risk Advisors LLC

T A B L E

6.1

Estimated risk measures for energy futures Risk measure

Long position

95% VaR 95% ES

2.995 5.180

95% VaR 95% ES

2.787 3.460

Short position

With jumps∗ 32.201 57.011 Without jumps† 2.978 3.818

Difference

975.2% 1,000.6% 6.9% 10.3%

∗Estimated with the same parameters as in Figure 6.1. †Estimated

with the same parameters as in Figure 6.1, except for jump size equal to zero. Estimates are based on 10,000 simulated Monte Carlo paths.


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MODELING SPREADS We now turn to the modeling of spreads. Spread relationships dominate physical markets and asset hedging activities. Yet despite their crucial role in most energy portfolios, the modeling of energy spreads is still very much in its infancy. Many firms still use pricing and risk models that make a number of clearly unrealistic assumptions about underlying market behavior. In particular, many firms continue to assume that joint price distributions are lognormal (or normal), that spreads are potentially unbounded, and use linear correlations to describe the dependence structure between related random variables. Each of these assumptions is highly questionable. The empirical distributions for many commodity spreads clearly show that these simplifying assumptions are inconsistent with real-world markets. These unrealistic assumptions introduce significant “model risk” that can result in serious mispricing of spread derivatives or the mismeasurement of the risks they entail. A variety of factors influence physical markets and help produce very “non-normal” distributions. One such factor is the availability of physical storage, which prevents the discount between spot and forward prices for many energy and commodity prices from ever becoming as large as standard models suggest is possible. Another is the availability of transportation, which links regional markets and sets bounds on geographical spreads. And a third is the ability of market participants to change their behavior depending on price signals (e.g. electric generators can reduce output when the “spark spread” between natural gas and power becomes too small). Approaches to Spread Modeling It is often more convenient to model the spread between two prices rather than the two different prices that determine the spread. Early attempts to model the spread assumed that it was lognormal or normal, and these assumptions make modeling easy because the only parameter that then needs to be calibrated is the volatility of the spread itself. The lognormal assumption implicitly assumes that the spread could never take negative values. Clearly, this assumption is unrealistic for many markets, and this approach is seldom used in practice. Another early assumption was that the spread variability in absolute terms would increase as the size of the spread increased. However, this is inappropriate because most spreads


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6.2

Basis between Henry Hub and El Paso Permian, PG&E Gate and SoCal Border

Source: SNL interactive. Analytics: Black Swan Risk Advisors

exhibit mean reversion (see Figure 6.2), which means that as the spread gets larger, the probability of it returning back to its reversion level increases. If we were to simulate spreads over long horizons without mean reversion, the simulated extreme levels of the spread would be clearly unrealistic and therefore produce the wrong price and risk signals. In the two-factor model, the prices of the two assets that determine the spread are usually assumed to evolve according to a correlated lognormal diffusion process (which may or may not involve mean reversion). Interest rates, volatilities, and correlations may also have a term structure, but they are assumed to be deterministic in this type of model. Two-factor models are the most common models used by market practitioners and have many appealing features. Besides the parameters governing each price process considered on its own, they require only one additional parameter—the linear correlation coefficient—to handle their stochastic interdependence. These models provide hedge ratios for each of the underlying assets that determine the spread, including vegas for the volatilities, and also allow for a hedge ratio reflecting sensitivity to changes in the correlation coefficient.

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6.3

Correlations are generally highly unstable

Source: SNL interactive. Analytics: Black Swan Risk Advisors

However, whereas these models assume the correlation to be constant, the empirical evidence indicates that energy correlations are far from stable. This instability is clearly illustrated in Figure 6.3. To be more specific, the correlation between energy prices is dependent on factors such as the following. ●

●

●

Time. Depending on the time period that we are trying to analyze (e.g. summer versus winter), the correlation will be different. This feature can be accommodated by using a term structure of correlations. Maturity. Depending on the maturity of the future contracts, or spot/future prices, the correlation will be different. Using a term structure of correlations to capture the dynamics of the relation between various forward prices along the same forward curve or inter-commodity spreads could also capture this component. Nature of the price shock. Depending on the nature of the price shock in one of the variables, the movement in the other series may be quite different (e.g. electricity and gas correlation under price spikes versus normal market conditions). If we use a linear


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correlation coefficient, we assume that the relation between two prices is linear, regardless of the nature of the price shock (shortterm versus long-term shock), and the context in which it takes place (e.g. storage or inventory levels). Market practitioners often respond to these issues by using different correlations for different purposes: they use correlations with ad hoc adjustments for time, maturity, different shocks, or different strike prices, and so forth. However, the fact remains that the two-factor model assumes that the “true” correlation is constant—and this highlights the deeper point that correlation is not an adequate measure of dependence in these sorts of problems. STRESS TESTS AND SCENARIO ANALYSIS The board and senior management should also appreciate the firm’s exposure to very damaging hypothetical events. This type of exposure can be evaluated using stress tests and scenario analyses. These are gaining increased acceptance among risk managers in energy and commodity trading firms. Indeed, owing to their significant advantages over other approaches, many risk managers now use stress tests as their primary risk measurement and management tool, complemented by VaRs as indicators of portfolio risk under more “normal” market conditions. Stress testing itself involves identifying possible events or changes in economic conditions that could have unfavorable effects on a firm. Having chosen a set of scenarios, the second stage in stress testing is to evaluate the impact of those scenarios on the firm. The results of these exercises can be very useful for strategic planning, capital allocation, hedging, and other major decisions. At a minimum, stress tests should also be employed to provide information about the effect of tail events beyond the level of confidence assumed by internal risk models. Firms should also bear in mind that regulators and credit rating agencies view stress testing and scenario analysis as necessary (rather than optional) complements to the use of internal VaR models. A good stress testing program is an absolute necessity for any best practices firm. Stress testing is becoming more and more sophisticated, and one of the challenges is to work out a rigorous way of applying different kinds of stress to portfolios in a consistent manner. For each stress category, the worst possible stress shocks that might realistically occur in the market

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are defined. To develop plausible and effective stress tests, firms should establish “stress test committees.” These are made up of representatives of the main groups in the firm, including trading, risk management, and economic analysis. The committee proactively identifies important scenarios and oversees the scenario analysis itself. The potential number of combinations of basic stress shocks is overwhelming. In practice, only a relatively small number of scenarios can be routinely analyzed. This means that the scenarios have to be selected according to the vulnerabilities of the particular portfolio. Each portfolio has specific characteristics that make it vulnerable to a particular scenario and/or stress tests. An energy derivative book that is short gamma is vulnerable to a sharp increase in volatility. Stress testing and scenario analyses are very useful in highlighting these unique vulnerabilities for senior management. Again, the choice is necessarily somewhat arbitrary. The usefulness and accuracy of the diagnosis that emerges from the scenario analysis depends on the judgment and experience of the analysts who design and run these scenarios. ORGANIZATIONAL AND QUALITATIVE ASPECTS OF RISK MANAGEMENT The theory and the practice of energy and commodity market risk management have developed enormously in the last decade, and one can easily get the impression that risk management in these areas is essentially quantitative. However, risk management is not purely, or even mainly, a quantitative subject. At the heart of market risk management is the notion of good risk management practice, and above all else, this requires an awareness of the qualitative and organizational aspects of risk management: a good sense of judgement, an awareness of the “things that can go wrong,” and an appreciation of market history. It also means that some of the most important principles of risk management actually come from disciplines outside finance, such as accounting (which tells us about subjects such as management control, valuation, and audit) and economics (which tells us about market behavior, among other things). So, while risk management makes great use of quantitative methods, the subject itself rests on a foundation that is fundamentally qualitative. In many ways, risk management is much like engineering: it uses sophisticated tools, but context and judgment are everything. And this, perhaps, is the most important thing for any budding risk manager to appreciate—especially one from a quantitative background.


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REFERENCES Blanco, C and Soronow, D. (2001) Jump diffusion processes—energy price processes used for derivatives pricing and risk management, Commodities Now (June), pp. 83–7. Clewlow, L and Strickland, C. (2000). Energy Derivatives: Pricing and Risk Management (London: Lacima Publications).

NOTES 1. A quantile is the quantity associated with a particular cumulative probability. Let p =F(x) be the distribution (or cumulative density) function for losses (where losses can be negative) over a given holding period. Now choose a confidence level (i.e. probability) c. The VaR is then given by the inverse of F(x) for p = c, i.e. VaR = F-1(c). 2. For more details, see Blanco and Soronow (2001) or Clewlow and Strickland (2000). We might also incorporate seasonal effects by adding dummy variables to (1) or by making parameters dependent on the season. If our model was applied to intra-day (e.g. hourly) periods, we could also do the same for intra-day effects (e.g. to take account of the times of the day when spikes are most likely to occur). 3. Apart from the properties of diffusion, mean reversion, and jumps, this process also has two other attractive properties—it ensures that the price is always positive, and (bearing in mind that the process is in continuous time) it permits an exact discretization when we simulate it in discrete time intervals. Without these properties, we will often get simulated prices that are negative, and the possibility of such “pathological” outcomes can undermine our simulation routines. 4. Parameter estimation is discussed in all the standard econometric textbooks and is by no means a trivial matter. However, for present purposes, we can take the parameter estimates as given.


C H A P T E R

7

Best Practices in Credit Risk Management for Energy and Commodity Derivatives Carlos Blanco, Kevin Dowd, Robert Mark, and Warren Murdoch

INTRODUCTION This chapter provides an overview of best practice counterparty credit risk measurement and management for energy and commodity derivatives. One of the keys to success in energy and commodity trading is the management of counterparty credit risk. This is particularly important in the energy markets, where recent high-profile disasters—such as Enron NRG, PG&E, Southern California Edison, and more recently, China Aviation Oil (CAO) Singapore—have highlighted the considerable dangers of inadequate credit risk management. Each of these cases involved a default that led to significant losses for the defaulting firm’s counterparties— losses that could have been avoided, or at least mitigated, by better credit risk management. Credit risk is generally (and rightly) considered more difficult to manage than market risk. The design and effective implementation of the right policies, methodologies, and infrastructure are prerequisites for good credit risk management. For example, credit analysts need to have access to measures of current exposure and potential future exposure as well as netting, collateral, and settlement information. Credit analysts also need access to a variety of internal and external tools to evaluate a counterparty’s probability of default (PD) and loss given default (LGD). Moreover, credit managers also need to watch their respective markets for market risk related news. 81

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INTERNAL RISK RATINGS: OBTAINING AND ANALYZING CREDIT-RELATED INFORMATION FROM COUNTERPARTIES At the heart of credit risk management is the firm’s internal credit risk rating system (IRRS). This is simply a way of organizing and systematizing the firm’s credit risk procedures so that credit analysts—both across the firm and over time—can arrive at ratings that are rational, coherent, and comparable. A robust IRRS should offer a carefully designed, structured, and documented series of steps for the assessment of each rating. Typically, an IRRS expresses an obligor’s PD in terms of a counterparty default rating (CDR). Such PDs are quoted as cumulative or marginal probabilities and are estimated across the term structure of maturities in order to evaluate long-term counterparty risk. Rating services, such as Moody’s KMV, Standard and Poor’s, and Fitch, provide PDs based on experiential data. (See the Box 7.1 for a sample of Moody’s KMV one-year expected default frequencies (EDF) for Delta Airlines and Williams Co.) It is important to point out that ratings are assigned primarily to public companies and private companies or structures that will pay the service to assign a rating. As a result, most private firms operating in energy and commodity markets are not rated. Therefore the credit analyst may not be able to benchmark their own internal rating against an external rating. This process is particularly difficult because the corporate and financial structure of a private firm is often quite distinct from that of a public firm. A commonly used type of PD in the industry is Moody’s KMV oneyear expected default frequency (EDF). The Moody’s KMV EDF metric is based on option theory and is influenced by stock price movement. In Figure 7.1 we can see the evolution of the EDF for Delta Airlines and Williams Co. In the case of Delta Airlines, the EDF increased considerably since 9/11 and is currently at its highest level. On the other side, Williams experienced a notable increase its EDF after the Enron collapse, but its EDF then fell sharply after the end of 2002. The second type of rating is a facility rating expressed in terms of the Loss Given Default (LGD). As its name suggests, this gives us an estimate of the loss we are likely to experience conditional on a default actually occurring. Thus the EDF or PD gives us an indication of the probability

CHAPTER 7 Best Practices in Credit Risk Management for Derivatives

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7.1

Parent company guarantees: analyze with caution To analyze the counterparty risk of an energy firm’s trading arm, it is important to understand the business and financial linkages between units within the same group and also investigate the potential for contagion effects. Parent companies may provide more transparent financial information than their subsidiaries but may not have a higher credit rating than an operating subsidiary. The parent may be public, the subsidiary private, or vice versa. However, the degree of support from a parent company to a trading subsidiary cannot be taken for granted, even if a parent company is hinting that it would support or even guarantee their subsidiaries’ liabilities. For example, many counterparties of CAO Singapore assumed that the firm had the backing of its parent company, a state-owned Chinese firm. However, the lack of transparency of Chinese companies makes that task complex, and this is particularly the case for stateowned Chinese companies. Credit risk analysts need to move beyond the analysis of the counterparty entity and in many cases evaluate the parents’ guarantees and their sovereign risk in terms of how it affects corporate credit risk. This ability to “see over hills and around corners” is one of the most important skills of good risk managers. In addition, correlations between defaults of parents and subsidiaries can be quite high depending on the legal structure and the nature of the guarantee, especially if it is made by a sovereign. In such circumstances the parents’ guarantees may be of little use. Parental guarantees should not be taken for granted and should be analyzed with caution by credit analysts.

of a default event, and the LGD gives us an indication of our exposure if a default event occurs. As a general rule, if the credit risk is large enough, then the approval of the credit exposure is reviewed by an appropriately qualified senior credit risk committee. One goal of this committee is to ensure that counterparties are rated using consistent criteria. The credit risk committee will also use its judgment to evaluate the reliability of the information provided by counterparties and identify gaps in the firm’s information about its counterparties. It will also typically examine various stress tests (or “what if?”) exercises in order to assess the credit risk that could arise due to crisis, contagion, or liquidity effects. HOW BAD CAN IT GET? POTENTIAL FUTURE EXPOSURE Best practice firms typically base their credit risk limits for derivative transactions on the sum of the current plus the potential future exposure.

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7.1

Evolution of one-year EDF for Delta Airlines and Williams Co. (a, b) Presents the evolution of Moody’s KMV EDF as well as Moody’s and S&P ratings for Delta Airlines and Wiliams. (c) Comparison of EDF for Delta to Quartiles for Air Transportation Industry. We can see that a significant number of airlines were facing very serious difficulties due to record high fuel costs. (d) Evolution of capital structure of Delta Airlines. The rising EDF for Delta is attributable to increasing market leverage, defined as the percentage of the market value of the business that is supported by debt

Source: Moody’s KMV

The current credit exposure is typically assessed using the current mark to market (MtM) value of the relevant position, net of accounts receivable, collateral guarantees, and legal netting arrangements. The MtM exposure is the loss we would experience if the counterparty were to default today. MtM values can fluctuate dramatically, so even a position with a negative current value can still represent a major credit concern. For example, our currently negative value might suddenly become positive, and the counterparty might then default.


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Dynamic Market-Based Counterparty Credit Limits Potential future exposure is typically evaluated using a Monte Carlo simulation engine that estimates the potential exposure at one or more points in time during the life of the contract. A simple and robust way to add an estimate of how much could be lost from each counterparty over a particular time horizon is to use a value at risk (VaR) style measure. We refer to this measure as VaR+. If we add our current VaR+ (or related measures such as expected shortfall) to our existing MtM exposures, we can then obtain a potential MtM exposure for a given confidence level and horizon. An example of such a VaR-enhanced counterparty risk report is given in Figure 7.2. In this particular case, we can see that even though the current MtM exposures with regard to BP and Chevron-Texaco are within our limits, adding in the potential fluctuation indicated by VaR+ numbers would lead to these limits being breached. The main problem with this approach is that VaR is usually best applied over short time horizons (e.g. one day) and in practice typically does not attempt to model positions dynamically. VaR calculations seldom take into account complex netting and margining provisions at the counterparty level. Therefore, it is important to supplement VaR+ measures with more sophisticated measures of potential future exposure.

F I G U R E

7.2

VaR-enhanced counterparty risk report


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Potential Future Exposure Modeling Our future exposure is the amount that would be lost at a given point in the future if the counterparty were to default on an in-the-money position. One measure of this future exposure is the potential future exposure (PFE), which is the projected potential exposure over the life of the transaction at given a degree of statistical confidence (e.g. 99%). We are particularly interested in two aspects of PFE. ●

●

The first is the maximum potential future exposure (MPFE), which is the peak or maximum of the PFE at a given degree of statistical confidence. The second is the expected exposure (EE) profile, which is the average or expected exposure at various points in time in the future. The EE is used for pricing credit charges into deals. It is also used to calculate the economic capital to be assigned to a portfolio.

The shape of counterparty exposure profiles is of special importance because credit exposures must be viewed over the entire life of the transaction. This is also important because different types of instruments and combinations of those instruments can generate very different credit exposure profiles over time. For example, a commodity swap’s MPFE typically occurs at roughly one third of the way through its life, whereas a commodity forward’s MPFE occurs at maturity. Broadly speaking, these exposures are subject to two different passage-of-time effects: the diffusion effect and the amortization effect. The diffusion effect describes the increasing probability that the value of a position will travel further away from its initial value over time, thus tending to increase the amount exposed to default. On the other hand, the amortization effect takes account of the fact that the remaining cash flows due on a position diminish over time, and over time this reduces the amounts that are exposed to default. Figure 7.3 shows both the expected and potential future exposures over the lifetime of a swap, with potential exposures at the 99% confidence level. The diffusion effect can be seen from the way that both types of exposures initially rise; however, over time the amortization effect dominates, and both types of exposure subsequently fall. The PFE analysis therefore tells us that our swap position initially has a relatively low exposure, but the exposure rises to a maximum at a horizon of about four years and then subsequently falls again


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7.3

Potential future exposure for a 10-year commodity swap


It is also very important that estimates of potential credit exposures take account of the most likely projected price dynamics features. One such feature is the possibility of price spikes, which can dramatically alter credit exposures in a very short time period. We can take account of such possibilities using a simulation framework that uses a realistic price process that can accommodate empirically important features, such as mean reversion and jumps, seasonal factors in the forward price curve, and changes in volatilities and other model parameters. Simulation methods are also good for dealing with the additional complexities that arise where we need to adjust valuations in illiquid markets (see Blanco and Mark, 2004) where credit is not explicitly priced. Simulation approaches are also particularly useful when dealing with multi-dimensional problems (i.e. where outcomes depend on more than one risk variable, such as prices, defaults, and recovery rates). As a rule, we can say that simulation approaches become relatively more attractive as the complexity and/or dimensionality of a problem increases. In addition, simulation methods can also take account of the extra complexities arising from credit mitigation methods: netting provisions, margining, collateral and guarantee terms, credit triggers, prepayment terms, and so on. The simulation framework can also incorporate other components, such as market liquidity conditions and operations

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risks—such as outages and pipeline blowups—which can be very important for energy firms. Owing to their complexity, PFE models should be thoroughly vetted before and after they are deployed for day-to-day use. End users need to verify the quality of the input and output from the PFE model. Input problems can arise due to inaccurate position level data as well as MTM-related issues. Output problems can usually be tracked to suspect numerical results. It is also very important to document the assumptions and steps in the calculations as well as to subject models to stress tests and back tests on a regular basis. Independent reviewers, such as internal and external auditors, should confirm that the models used are suitably calibrated and implemented. Testing of PFE models is a continuous process. Users need to demand to see test results and should be encouraged to participate in stress testing. Further, senior management needs to be aware of the assumptions on which they are based and of the potential pitfalls with their use. COUNTERPARTY CREDIT RISK CHARGES It is good practice—but still not common in many energy and commodity trading firms—to charge for counterparty credit risk. After all, counterparty credit exposure is not free and can result in considerable potential losses for the firm. Charging for such exposures helps to create the right alignment of incentives within the firm. For example, if traders are charged for the counterparty credit risk taken, they then have an incentive to manage it properly in the broader interests of the firm. On the other hand, if traders are not charged for credit exposures, they have no incentive to be careful with the exposures they take on. Instead, they will be tempted to take on “cheap” deals that only appear to be cheap because no one is taking account of the implicit costs of the credit exposure they create. It is self-evident that if the energy firm is not aware of this hidden credit cost, then it could acquire large hidden credit exposures to poor-quality counterparties. Of course, developing a system of counterparty credit risk charges can be a complex task, and the cost of inaction can be very large indeed. CREDIT LOSS DISTRIBUTIONS A credit loss distribution (CLD) is a probability distribution of losses from a credit portfolio. In a frequency diagram, the loss distribution would have


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the frequency of losses on the vertical axis and the actual loss of the portfolio on the horizontal axis. A CLD can be used to estimate the expected loss (EL) over some specified horizon due to expected fault or credit downgrade. To calculate EL for a single exposure, we need measures of exposure (PD), recovery rate (RR), and LGD over a fixed horizon. Exposure, PD, RR, and LGD are typically expressed over a one-year horizon (Figure 7.4): ●

●

●

●

Exposure (E). the part of the portfolio that is subject to loss, expressed in current dollars. Probability of Default (PD). The expected default probability prior to maturity Recovery rate (RR). Amount of the defaulted position that is likely to be recovered Conditional Loss Given Default (LGD). The amount that is likely to be lost if the counterparty defaults.

The EL for a portfolio is simply the sum of the ELs for each counterparty. A CLD can also give us the unexpected loss. The unexpected loss for a single asset is a function of the variability of the exposure, the LGD, and the PD. In addition, the unexpected loss of a portfolio is also a function of the correlations of different risk factors. Expected and unexpected losses are illustrated in Figure 7.5. Needless to say, how we model the CLD will have a major impact on the tails of the distribution (including the unexpected loss) and therefore have a major impact on the firm’s capital requirements. ECONOMIC CAPITAL AND CREDIT RISK Economic capital is the financial cushion that a firm uses to absorb unexpected losses, such as those related to market (e.g. adverse market moves), credit (e.g. credit downgrades and/or default), and operational F I G U R E

7.4

Credit loss distribution

Source: Black Swan Advisors LLC

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F I G U R E

7.5

Credit loss distribution, expected and unexpected losses

Source: Black Swan Advisors LLC

events (e.g. plant outage). The calculation of the aggregate economic capital relies on the integration of the market, credit, and operational loss distributions. The expected and realized returns on economic capital can be compared for the different activities of the firm. The implementation of a comprehensive and well-understood risk-adjusted return on capital (RAROC) program can provide the bridge to tie risk–reward relationships with the different activities and business units of the firm and therefore serve as a common analysis and communication tool. An investment evaluation process based on economic capital considerations (such as where decisions are based on a risk-adjusted return basis) encourages corporate managers to take risk into consideration explicitly at the time of allocating resources internally as well as to make investment and divestment decisions. For example, energy-related companies would benefit from regularly publishing for key stakeholders their business specific return on risk capital versus income compound annual growth performance measures (similar to that produced by Citigroup and shown in Figure 7.6). There are three main types of economic capital. Each measure is a function of the scope of the risk interdependencies and each can be very valuable as a decision-support tool in different business contexts.


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7.6

Return on risk capital versus income growth for Citigroup

Source: Citigroup. Risk Capital and Capital Allocation, March 29, 2004

Stand-alone economic capital is defined as the amount of capital an individual risk-taking activity would require if it was independent of the rest of the firm. Stand-alone economic capital is traditionally used to evaluate the performance of managers responsible for maximizing the riskadjusted returns of that particular activity. Marginal economic capital is the amount of capital that each business adds to the entire firm’s capital requirements. Marginal economic capital can also be interpreted as the amount of capital that would be released if we decided to divest from a particular activity. Marginal economic capital can be used for acquisition and divestment decisions after taking into account the expected returns from those decisions. Diversified economic capital is the amount of capital allocated to each risk-taking activity as a part of the overall firm. Diversified economic capital is calculated by taking into account the interdependencies between different risk-taking activities within the firm, and is similar to a portfolio beta.

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Marginal economic capital can be used to measure the contribution each risk-taking unit makes to overall risk and therefore for internal capital allocation within projects. As far as the aggregation of market, credit and operational risk for the calculation of economic capital, there are four main approaches. 1. Perfect inter-risk correlations. This approach assumes that the aggregate economic capital is simply the sum of market, credit, and operational risk. It is very easy to use but also very crude because it ignores any diversification across these different types of risk. 2. Joint normality of risks. If we assume that risks are normal (or near normal), then risks can be integrated through a correlation matrix. This approach is still commonly used, but it can lead to large estimation errors in economic capital due to the non-normality of most credit and operational risk distributions and the non-linearity of their dependence. 3. Joint simulation of market, credit, and operational risk. We can simulate market, credit, and operational risk factors jointly to arrive at a single loss distribution. Even though it is theoretically appealing, it is not used by market practitioners due to the difficulties of creating realistic joint simulations of market, credit, and operational events. 4. Copula approaches. This approach allows for each type of risk to have its own marginal risk distribution; the dependence structure is then modeled with copulas. There are a wide range of copulas to choose from, but empirical research has shown that the better copulas for credit risk problems are the Gaussian copula and the Student t copula. Rating agencies expect firms to hold reserves against these unexpected losses at a given confidence level (e.g. AA) in order to achieve a particular credit rating. The higher the credit rating desired, the lower the probability of incurring losses above the capital level over the period corresponding to the credit risk horizon and the higher the necessary capital. More precisely, the firm needs to ensure that it has capital adequate to bring the probability of a catastrophic (i.e. insolvency-inducing) loss down to a level compatible with its desired credit rating.


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CONCLUSIONS The increasing complexity of the credit risks involved in running an energy or commodity trading operation mean that enterprise-wide credit risk education and awareness should be a critical item in the agenda of credit risk takers, senior executives, and board members. Anyone who has any doubts on this should consider how easy it was to lose money as the counterparty of companies such as Enron or CAO. Firms that fail to take counterparty risk issues seriously are therefore taking large gambles—and generally without their senior managers even realizing it. REFERENCES Blanco, C and Mark, R (2004) Credit risk management for energy firms in an EWRM framework, The Risk Desk, September, IV (IX). Blanco, C, Dowd, K, and Mark, R (2005) Russian Roulette, FOW, April. Crouhy, M, Galai, D, and Mark, R (2000) Risk Management (New York: McGraw Hill).


C H A P T E R

8

Introduction to Natural Gas Trading Frank Hayden

WHAT IS NATURAL GAS? Methane, otherwise known as natural gas, is an unusual type of commodity. Unlike electricity, which will kill you if you touch it, or gold, which is nice to feel and has that deep yellow color, natural gas is colorless, odorless, difficult to measure, difficult to ship, and easily disappears into thin air. On the other hand, natural gas does have some good qualities; it is often used in cooking, heats homes, and is good for the environment upon consumption. Consumption is one of the unique qualities of natural gas. What does it mean? And how is that important when looking at it from a risk management perspective? Gold is not “consumed”; it can be used, but after its use it continues to reside in the finished product. Raw gold is taken into a manufacturing process and turned into a gold watch. The commodity “gold” continues to have its value persist even after it goes into making a watch. This can have dramatic effects on its market/price fundamentals. Storage costs become the carrying costs associated with warehousing. Natural gas does not linger after the cooking or home heating process. A heated house is only of any use in cold weather if the house stays warm. Storing natural gas becomes not only a function of the carrying costs associated with holding it, but also the injection costs and withdrawal fees getting of it out of the ground. (Natural gas is stored under the ground in leached out salt domes or depleted oil fields.) The fact that natural gas is 95

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consumed sets the stage for articulating the fundamental demarcation in natural gas—the physical and financial markets—each with its own behavioral characteristics and all of which are borne out in how it trades. CONSUMPTION COMMODITY The supply–demand curves between consumption (physical) and financial markets are very different. Natural gas may even be considered unique in that it has two active markets that trade on top of each other. This results in both supply and demand being more elastic in a financial market than a consumption market. This is because the consumption market often exhibits feast or famine characteristics. This can explain how price spikes are more apparent in the cash market (prompt = consumption) than the forward (financial) market. Either you have so much of it you start dumping it anywhere at any price, or you do not have enough of it, and you pay any price. Compounding this type of behavior is the unique way in which natural gas is transported. It is shipped in a pipeline, which can often be at full capacity in peak periods, and requires infrastructure to distribute. This inelastic behavior is far more evident in the history of consumption commodities than financial, yet it still exists in both. Natural gas markets over the years have evolved so that two markets exist side-by-side—consumption (or physical) and financial. The essence of the natural gas business is that it captures the value proposition presented between these two markets, given the general inability of the supply–demand curves to synchronize at all times. If you consider the forward NYMEX curve, part of the curve is consumption-driven (physical) and part of the curve is financial (financial), and mastering the intersection of these two elements can create profits. How does one think about this? The wonderful thing is that the market has developed in such a way that forward pricing is fully transparent. Fifteen years ago, finding a forward price in natural gas was nearly impossible. It was fairly easy to determine prices for the coming month, but not for something that was eight months into the future. That has now changed. PIPELINE GRID As mentioned earlier, natural gas is not something you can put into a bucket and move from point A to point B. Rather, it has to be shipped in pipelines, from areas of production to areas of consumption. Pipelines are expensive to build and maintain. Most pipelines start at production

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basins and flow to large marketplaces. Traditional US supply basins include the Texas/Oklahoma, Rocky Mountain, and Gulf of Mexico regions. Typical markets for natural gas include California, Chicago, and New York—all the major metropolitan areas. In peak periods the pipelines moving gas from the supply regions to market regions can be at full capacity. These pipeline constraints impact pricing economics differently than a commodity that allows a truck or bucket or boat to pick up and drop off as desired by the end user. A majority of the consumption areas experience winter weather, and a majority of the supply areas never do. This is critical as weather may shift the demand curve. In the past, demand has been such that the supply basins had enough gas by themselves to meet demand; this was a period known as the gas bubble. Today, however, winter demand must be supplemented with storage. Storing gas through time, and related forward hedging activity around that activity, can give rise to some complex gas storage modeling issues. Storage not only helps to meet unexpected heating/ cooling demand, but also gives the physical market an option to provide those molecules—and it may cause shifts in the supply curve. As an aside, storage is a type of time spread option, modeled over multiple time periods at multiple locations. This model considers volatility, strike price, injection and withdrawal fees, constraints on how much can be put in the ground and how much can be taken out of the ground, the frequency of the injection/withdrawal cycle in a year, and correlation along time and at locations. Because storage is bounded by the physical constraints of the facility, it can get fairly complex. SUPPLY AND DEMAND Figure 8.1 is a supply–demand curve from the economic class we all had in college. As a refresher, as demand changes, the price moves, and a new equilibrium is established. Conversely, these changes can happen from the supply side. For natural gas, supply is more static, while demand tends to fluctuate given weather conditions. As more supply is made available, the supply curve shifts out, generally decreasing prices, unless, of course, it is offset by increases in demand. This all sounds quite simple, except when you impose two fundamentally different markets on top of each other and argue convergence at NYMEX expiry. A fundamental concept in futures trading is that the underlying physical market will converge with the overlying financial market. What is not explained, but rather learned in the school of hard knocks, is that this “condition” exists only at expiry.

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F I G U R E

8.1

Supply–demand curves

Between the future and today, the financial supply–demand curves vary from the physical supply–demand curves (see Figure 8.2). FINANCIAL MARKET It is worthwhile to review the financial market, what it is and what it does. The financial market as traded on the exchange represents global

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8.2

Physical and financial supply–demand curves


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world opinion about commodities. The currency futures market represents the global opinion about the price of a currency in the world market. The transaction between two dealers only represents the summation of the opinions of those two dealers. At the moment a deal is struck, both participants feel like they made a good deal. It is the best time to finalize contract negotiations and congratulate each other on the excellent trade. It is as the deal progresses through time that each participant begins to realize that they are losing or making money. That is the differences between the global opinion and two dealers. Similarly with natural gas, the NYMEX natural gas futures market represents worldwide opinion regarding the price of natural gas at Henry Hub (the NYMEX contract delivery location). In other words, it is the summation of all global expectations at any given moment about the price of natural gas. Let us examine that market. What factors drive the price economics and in turn the financial market fundamentals? The supply–demand factors that impact these curves hinge on (1) access to the marketplace, (2) the ability to transact in that marketplace, and (3) acceptance by the participants that this is a standard for price discovery. Access to the marketplace comes in a couple of features, one of which is lowering the cost of entry, making it more accessible. The ability to transact comes in the form of opening the market up to participants from all walks of life. Market acceptance comes from participation and acceptance that this is the standard through branding and from being the standard bearer. All this together means it is easier to trade. The fewer hurdles the exchange puts on its participants, the more people there are voting with their checkbooks about the price of natural gas. This is true for both the supply and demand sides of a financial market. I think the cornerstone bringing this together is reputation. (In my business model, all risk gets wrapped up into “reputational” risk.) As people accept that this market is the market, the more supply there is, and the more demand there could be—all of which can cause shifts in the supply–demand curves. The financial exchanges have to continue to do their job of: (1) lowering cost of entry, (2) increasing the public’s acceptance of their standard/branding, (3) increasing the accessibility, (4) making it more fungible/transparent, and (5) providing valuable services to participants. The ability to transact in that marketplace is an important barrier that once conquered can increase the number of players that potentially demand or sell natural gas. Barriers of trade include everything from

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financial products being offered in the marketplace to the amount of margin the exchange requires to maintain position. Lastly, the general acceptance in the marketplace, the “branding,” enables a higher confidence level. The greater the confidence level in the financial market, the greater the willingness to participate in that market, naturally impacting the supply and demand curves. It is critical to recognize that these factors impact the supply of buyers and the supply of sellers (supply–demand) curves of the financial market. This will become more critical as we examine the impact of NYMEX’s market clearing mechanism. I postulate that this market clearing activity shifted the supply and demand curves out, increased NYMEX volume, and brought about greater NYMEX natural gas futures branding. This ease of access, lowered cost of entry, and increased branding (through offering credit facilities) made the locational natural gas financial basis market more accessible, brought about greater transparency to those locations, and provided a valuable service of credit support to its participants. It is a success story for the financial market. As NYMEX has introduced new products (access, e-minis, NYMEX cleared products, etc.), it has also caused shifts in the supply and demand curves of the financial market—all creating opportunity for profit and loss. (Keep in mind the current low-interest-rate environment, combined with dismal stock returns and a bearish outlook in bonds; all this has created a greater number of people looking to invest in or trade futures, leading to further shifts in these financial market fundamentals.) The discussion to this point has examined the supply and demand fundamentals of the financial market. There are statistics published by the US Commodity Futures Trading Commission and NYMEX that help in understanding this better. Open interest, volume traded, option skew, and price levels (NYMEX futures prices as well as locational cleared/settled prices) taken together can help one to understand the economics of the financial market. Consider what the headlines and newspapers say regarding the increase in hedge funds in trading. This helps the financial market be very efficient and provides transactional liquidity to all participants. Additionally, it is a global market. A trader on vacation in Tahiti can transact as well as an oil man in Bahrain and a movie star in California. All these diverse participants help determine the supply and demand in the financial futures market. Remember, the futures market is the world’s largest producer and—simultaneously—end user of natural gas. And it is only a financial market.


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THE PHYSICAL MARKET MEETS THE FINANCIAL The physical market also has supply–demand curves. These curves appear more inelastic than the financial curves because they deal with infrastructure, capital budgets, end users, and residential customers. On the other hand, they can be a lot more elastic in the sense that people can decide to live in a cold house or eat gazpacho all day! It is important not to lose sight of the fact that at the point of consumption, price may become irrelevant (inelastic). The reasons for this are that: (1) you really do need to heat your home and cook your food, melt your steel, or make your fertilizer; (2) very seldom is the consumer given a pricing signal about that marginal unit (often, these units are purchased by someone else, and 30 days later, you feel the sting in your wallet); and (3) life goes on, repeating point number (1): you really do need to heat your home, cook your food, or supply that extra megawatt to the power grid so that there is not a blackout. Sometimes it is more expensive not to consume that marginal unit than to consume it—the paradox of a feast or famine market. So let us take a look at the supply–demand curves for the physical market. Housing, new builds, demographics, new production fields, production declination curves, hurricanes, well freeze-offs, storage, liquid natural gas, and weather—all come to play in the physical market, and all can cause shifts in the supply–demand curves. Some of these are predictable, and some are not. At Expiry A critical concept and principle of natural gas futures trading is that at expiry the physical and financial markets converge. Buyers and sellers will arbitrage the differences in price until there is none. This creates opportunities whereby the physical player can take advantage of the pricing offered by the financial market (or vice versa). The general concept is that if the financial market perfectly nailed the price (supply/demand) physical fundamentals at expiry, the price would not change significantly. Most price changes under this condition would represent transactional liquidity supply–demand fundamentals rather than actual physical market supply–demand fundamentals. If one were to review natural gas pricing action at expiry, typically, the physical supply–demand market is under- or overestimated by the

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F I G U R E

8.3

Condition at expiry

market participants. This yields an exciting time given the wide price swings. The critical point is that these economical fundamentals have to superimpose at expiry (see Figure 8.3). The Future Forward Regarding the future forward months, this market is dominated by a mix of financial and physical players. Markets do not have to converge; they do not have to do anything except determine a market clearing price whereby global opinion agrees to buy and global opinion agrees to sell. It is the job of those who transact in the forward market to properly understand the fundamentals of the nature of what they are doing. Forward transactions involve managing global opinions regarding natural gas as well as supply–demand fundamentals. Often, the big wild card becomes weather. Figures 8.3 and 8.4 illustrate: (1) the condition at expiry and (2) the condition at future forward. Notice the difference in elasticity. That difference in elasticity is due to the number of participants and varying degree of global opinions. The condition at expiry has: (1) less financial participants and more physical players, (2) clearer physical supply– demand fundamentals, (3) physical constraints/considerations, and (4) the need to consume or not consume (that is the question ... for you Shakespeareans) (see Figure 8.4).


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8.4

Condition at future forward

Weather I once asked a weather person about the accuracy of his modeling. He told me that he is generally highly accurate in the next 24 hours, but further out in time, his accuracy begins to falter. His ability to forecast rain on the Wednesday three or four weeks out falls off dramatically. Then he said something surprising. His accuracy begins to improve once you start considering seasons. He said that his ability to forecast cold or hot summers usually peaks around three months out and then it starts decreasing. Weather forecasting becomes a critical feature in understanding supply–demand fundamentals that ultimately determine price, especially considering the physical constraints mentioned earlier, such as pipelines. Take critical note of differences in situations where you can put it in a bucket or ship as inelasticity of supply decreases and so too does the variability of price around weather. In other words, weather is a feature, given the natural gas infrastructure. Oil, while being weather-sensitive, tends to trade on macro supply–demand curves, rather than the micro. In summary, the accuracy of model forecasting is an important consideration in trading. Traders get paid for taking risks, and as such, the riskiest part in a model forecast is the nearby future. The nearby future time period is where the supply and demand curves for both the physical and financial markets are snuggled closely together. This is the trading opportunity (see Figure 8.5).

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8.5

Model forecasting and trading opportunity

CHARACTERISTICS OF NATURAL GAS RISK In general, there are four risk types that define natural gas characteristics—price, basis, correlation, and index. Price This represents exposure to the NYMEX price action. This risk exposes the firm to margin calls, gain/loss, transaction liquidity, funding liquidity, credit risk, value at risk, and potential forward exposure. It sums up how people make or lose money. There is no free money; price risk sums up how money is made or lost. It is the job of the risk manager to quantify the risk taken and articulate this risk such that the people making the capital allocation decisions can make them effectively and in the know. One can control certain variables and certain decisions that one makes. The critical thing is to have the proper analysis such that the view on price is not so much a gamble but rather a determined input into your business strategy and risk-taking activity. Two wrongs do not make a right and may ultimately spell loss. Basis Broadly speaking, “basis” refers to the differential between a primary price standard bearer and a secondary location. In the United States this term refers to the differential to NYMEX that other locations in the United States gas markets trade off. As European gas markets mature, there will


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be more basis risk to manage there as well. This risk can be more difficult to manage, forecast, and understand because it involves three supply–demand curves: those in the financial market, those at the Henry Hub (the physical delivery point for the NYMEX natural gas futures contract), and those at the physical location that is a basis off NYMEX. Generally speaking, the basis differential is less than the full price, and as such, the math suggests that this risk is smaller. However, whenever one item is being bought and another sold, it really says that the trader is anticipating that what he bought will go up faster than what he sold or that what he sold will go down faster than what he bought. In other words, the trader may be arguing that his value at risk is lower, but in reality the risk transformed itself from price risk to correlation risk. Correlation In a short essay such as this chapter we can elevate an issue and propose a method to quantify and manage. The issue is rather simple, and it is brought out on correlation risk. The issue is that when anyone buys one thing and sells something else against it, unless it is the same product, instrument, location, reference date, and carries with it the same volatility, the trade introduces correlation risk that may or may not be modeled in risk management systems. Simply stated, buying apples and hedging it with oranges creates an apple-to-orange correlation. Granted they are both grown on trees, both subject to rainfall, and may on the surface appear like a reasonable hedge, but further reflection will bring to mind that the weather in which an apple grows is very different than the weather in which an orange grows. There are vast differences, but suffice it to say that the correlation becomes the trade du jour. Stress loss risk and a robust stress loss suite of stress testing are probably the best tools to quantify and a risk manager’s best friend. Index Floating price risk is index risk and represents the legal obligations to supply and deliver natural gas rather than the price risk. Upon pricing, floating risk becomes fixed price risk. This risk has an interesting characteristic in that as the delivery month approaches, it can take on a more fixed price component. Step back in time, when there was no Internet and right after FERC Order No. 436—this is when natural gas prices and

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delivery schedules were negotiated over a course of a week. It was the week before the end of the month when end users, producers, and middlemen discovered prices and scheduled deliveries for the coming month. Soon this became known as “bid week.” It is during this week that index prices begin to get set. During the last week of the month, the natural gas index price gets determined, and it is the same week that NYMEX prompt natural gas futures contracts expire. Generally, the rule is that if everyone thinks prices are going up, prices are set early (floating price risk changes to fix price), and conversely, if everyone thinks prices are going down, prices are set late in the week (more or less the day of or right after NYMEX expiry). This characteristic brings interesting challenges to the risk system in how this is captured, quantified, and reported. There is no such thing as free money. Traders want great returns on risk capital, and spread trading typically reduces value at risk (yielding better returns given limitations of measurement). This introduces a type of risk that is difficult to quantify. Cross correlation becomes higherdimensional things that only rear their ugly heads in real time, after you’ve been spanked. This type of risk capital is best measured under stress conditions. Spread trading is a form of stress capital deployment—something organizations are typically poor at measuring and reporting, which ultimately means it is not reported, which ultimately means value at risk is reduced and returns are pumped up. This is very similar to the real estate speculator who says that no one knows how to quantify what is behind the walls in the building you are about to buy; it really is tough/bad luck if it turns out to be asbestos. Traders buy and sell, and if they have not exited the trade, the chances are they are introducing the firm to a different type of risk. HOW THE US GAS INDUSTRY DEVELOPED In the beginning the natural gas industry was regulated from the wellhead to the burner tip. Producers sold gas to pipelines at regulated prices, and pipelines sold gas to end users or local distribution companies at regulated prices. The really big end users, such as the local distribution companies serving the major metropolitan areas, basically paid for the pipelines to be built to supply their cities with natural gas. There was a commodity charge, a take-or-pay charge, and demand charges. Ideally, take-or-pay charges were sleeved from the producers to the end users, with the pipelines being in the middle.


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In the mid-1980s, the industry was quasi-deregulated. The end users were allowed the opportunity to buy from the spot market. This created problems because the demand that had been buying gas from the pipeline could now buy it from the spot market. This meant that the pipelines had to “pay” the producers, even though they were no longer taking their gas. Conversely, the end users had the law on their side, freeing them up from any take-or-pay obligations. During this time, some pipelines filed bankruptcy, producers sued, and everyone argued for restructuring. Gradually, the wellhead prices became deregulated, which freed up the pipelines to ship gas, the end users to buy gas, and the producers to produce gas. Additionally, this set up the supply and demand curves for physical and financial players. In the beginning, there were mostly physical players and very few financial players. Financing in the industry was dead; there was a gas bubble, and everyone would wonder what the price of gas in south Louisiana had to do with their production in the midcontinent. Gradually, the NYMEX natural gas contract gained acceptance, and the futures contract branding began. The supply and demand of participants increased; a new market was born. Soon everyone knew that the price of gas in south Louisiana had a lot to do with its production in west Texas, California, and the Rockies. Natural gas futures became more and more accepted, and the transparency it brought seemed to be a good thing. Volume increased, and soon participants could no longer make enough phone calls to execute their deals within the constraints placed on them by the pipelines. Remember bid week. Soon the pipelines began accepting nominations for the next day. You no longer were required to supply a monthly schedule; you could now provide a daily schedule. The cutoff time for schedules was moved up to 11:00 a.m. Central Standard Time, the day before delivery. Meeting this constraint became increasingly difficult. People were hired to make the required phone calls before the 11:00 a.m. deadline. Soon this activity was moved online and conducted electronically. Many companies offered an online “click and go” service. Enron was one such company; another was ICE. All this made natural gas more fungible, increasing the number of users. It made the global worldwide opinion of natural gas very different from that of the local supply–demand curves, creating opportunities to trade.

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WHERE ARE WE GOING? Fast forward, past the Enron debacle, the following credit crunch, and into the awakening of worldwide demand. Everyone wants a car; everyone wants a life full of energy, energy produced from hydrocarbons. Log on to a webcam in Hong Kong or New Delhi, and what do you see today that you didn’t see 15 years ago? Cars. Step into September 11, war in Iraq, worldwide demand, low returns, NYMEX clearport, and pending bear market in bonds—all this brought new players into the market. Yes, the supply–demand curves of the financial market shifted again. Worldwide expectations of what prices should be and are versus their related implied supply–demand fundamentals can and have varied greatly from the location supply–demand in the physical market. New participants have come into the market, increasing the supply and demand from the financial participants. At the same time, finding physical players is getting more and more difficult. If we presume that physical players are “masters” of the supply–demand curves at the consumption/end user level, and if we presume the financial players are masters of the supply–demand curves at the macroeconomic level, opportunity becomes created. Gas market participants come and go, while the infrastructure and daily needs of the population seem to go on and on. What does this mean? We know from the statistics published in various sources that the failure rate of people who trade can be as high as 95%. A majority of people lose money when they trade. Does this mean that as time goes on, the losers tend to leave and go on to other types of activity, or that they morph into something else? Rather than forecasting the demise of the hedge funds and/or the speculator, I will forecast an increase or shift in the character of the physical player. Formerly, many physical players were really middlemen, smart and on top of logistics and the needs of the producers and end user community. The trend is for much of this activity to get absorbed by the producer community. Producers are striving for some kind of branding. I think this branding effort will continue to move producers away from simply drilling it up to more of a direct selling role. This ultimately may impact the physical players’ activity in the financial market. In other words, it is said that Exxon does not use futures to hedge; rather, it uses its retail arm. It could be the case that physical players will use their direct sales to hedge and slow down their activity in the NYMEX markets. I do not really think this is true because forward hedging can be a very credit-intensive venue—one that the financial exchanges manage best.


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I predict that the number of hedge funds participating in the NYMEX natural gas futures market will decrease to a few mega-players. These mega-players will be active in both the financial and physical markets at a ratio of roughly 80% financial to 20% physical. I believe producers will continue to invest in getting the end user subscribed but will have limited success given the credit constraints and requirements of forward branding and hedging. Additionally, I think niche physical players will evolve such that the need to be all to everyone goes away.


C H A P T E R

9

Structured Transactions in Natural Gas Mark Houldsworth

INTRODUCTION This chapter describes structured transactions in natural gas and how these transactions can be valued. It builds on the background material on natural gas markets in Chapter 8. For the purposes of this chapter, “structured transactions” means transactions that require valuation and hedging using options of one sort or another. Simple linear transactions, such as index transactions or fixed priced transactions, are not addressed. Furthermore, since the focus is on transaction structures, we will discuss possible stochastic processes and models only as they relate to the structures reviewed.1 The chapter does not generally review stochastic processes per se. The chapter begins with a discussion of natural gas storage. Natural gas storage is an especially complex transaction that markets must address in moving molecules from one time period to different possible time periods. We will see that a thorough review of natural gas storage valuation actually makes it easier to consider other financial and physical transaction structures that are typically simpler and are often subset problems. The chapter follows the storage discussion with descriptions of swing options, Asian options, and swaptions. The reader will note that this is a non-exhaustive list of structures that might have been more prevalent, say, at the beginning of the present decade. There are two reasons for this. 111

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First, there are many good resources for valuing these structures, and there is little to add. Second, our assessment is that with the collapse of Enron and the promotion of Sarbanes–Oxley rigors, many of these structures trade only sporadically, if at all. NATURAL GAS STORAGE This discussion of natural gas storage valuation considers only unregulated storage, where owners have the right to fully exploit calendar arbitrage. Much of the storage that exists today was developed and continues to move directly into highly regulated markets. Charges for these assets are typically not related to the carry in the market. The behavior of owners is typically driven more by reliability concerns than by arbitrage. The owner of unregulated natural gas storage has the right to inject and store gas at one time and withdraw this gas for sale or use at a later time. It is a bundle of real options. The specific nature of these options is governed by physical or contractual constraints that govern what one can and cannot do over the life of the asset. The typical constraints found in a storage contract are shown in Table 9.1. In addition to physical constraints on what one can and cannot do, the economics of storage is also determined by fuel charges, which are assessed for injecting and/or withdrawing fuel, and also variable charges assessed against injecting or withdrawing. The more constraining the storage contract, the less valuable the options, and vice versa.

VALUATION TECHNIQUES There are four basic valuation techniques: 1. 2. 3. 4.

intrinsic valuation; stochastic dynamic programming—trinomial forest; stochastic dynamic programming—least squares Monte Carlo; optimal basket of spread options.

The first three all involve dynamic programming. A dynamic programming asset is an asset where what you do today affects what you can do tomorrow. If one fills up storage today, for instance, one cannot inject tomorrow. If one withdraws storage down to zero today, one cannot withdraw tomorrow. This characteristic of natural gas storage requires

CHAPTER 9 Structured Transactions in Natural Gas

T A B L E

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9.1

Natural gas storage constraints Constraint

Description

Capacity

The maximum amount of gas the option owner may hold in inventory at any point in time. The maximum rate at which the owner may inject gas into the capacity, often stated as a percentage of the capacity. Specific rules that dictate the maximum permissible injection rate as a function of the inventory. They restrict that maximum injection rate as one’s inventory nears capacity. The maximum rate at which the owner may withdraw gas from inventory, often stated as a percentage of the capacity. Specific rules that dictate the maximum permissible withdrawal rate as a function of the inventory. They restrict that maximum withdrawal rate as one’s inventory nears zero. Constraints that require the owner to inject or withdraw certain quantities at certain times. Constraints that require the owner to maintain either minimum or maximum inventories at certain times over the life of the contract.

Maximum injection rate

Injection ratchets

Maximum withdrawal rates Withdrawal ratchets

Must inject/must withdraw constraints Minimum inventory / maximum inventory constraints

backward thinking. Analytically, this characteristic of storage invokes Bellman’s principle of optimality for multi-stage optimization problems. This states that from any point on an optimal trajectory, the remaining trajectory is optimal for the corresponding problem initiated at that point. Intrinsic Value The intrinsic value is the amount by which the storage option is in the money at a particular valuation date, ignoring optionality. It is the dynamically feasible and optimal spread quantities multiplied by the discounted absolute price spreads less the fees and expenses required to physically exercise the spreads that exceed the strike. If these hedges are placed, the user will not receive less than this value over the life of the asset. If these

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spreads are put on and left alone, the user will not receive more than this value. Mathematically, this problem can be stated as follows: VS(t , inv) = max E t [(1 − e − rt )(VS(t + dt , inv + injwd (t )) − injwd (t ) P(t ))] injwd ( t )

Here VS(t, inv) is the value of storage at time t, conditioned on the inventory level, inv, at that time; P(t) is the cash impact of changing the inventory level over dt, including fees and fuel impacts; and injwd(t) is the injection (positive)/withdrawal variable to describe activity over dt. If this equation is true for every VS(t,inv), then it is also true at the beginning of the analysis for VS(0,0).2 It is critical that VS is measured at every feasible inventory node as the maximum value of the cash impact at that time, plus the continuation value for the relevant implied forward inventory state. The first task in establishing the intrinsic value is to absorb the physical or contractual constraints of the contract and prepare a forward feasible inventory state map. Figure 9.1 shows a possible inventory state map wherein one cannot inject fully in one period or withdraw fully in one period. It is not high cycle storage. Each of the inventory states shown

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9.1

Illustrative feasible inventory states


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should be understood as feasible. Nothing indicates what is optimal at this stage. The next step in the analysis, moving from the back, would be to value each node until one reaches the front node, where the initial inventory state is known with certainty. The continuation value at the last time step is naturally zero. As one moves backward in time, though, each inventory node receives a value that is equal to max (feasible inventory change value plus continuation value resulting from change). From any interior node there will be a number of feasible injections and a number of feasible withdrawals. Each feasible injection or withdrawal considered will be associated with a continuation value located at the inventory state one period hence that results from the injection or withdrawal considered. Figure 9.2 illustrates the backward evolution of these values. High inventory states show large cash values because they ignore the expense of injecting gas to generate the inventory. Ultimately, the value of injecting is absorbed in building the inventory continuation values. For instance, Figure 9.2 shows nearly $20 million for full inventory states given the forward curves. As the analysis moves backward in time to absorb the injections, the intrinsic value here becomes about $250,000.

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9.2

Optimal value by inventory state and time

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Stochastic Dynamic Programming: Trinomial Forest In calculating the intrinsic value one assumes the forward prices are known with certainty. Continuation values are therefore not stochastic. Stochastic dynamic programming relaxes this assumption by invoking a stochastic process that draws the forward prices, and continuation values, stochastically. One has certainty regarding the current price state, but uncertainty is manifested in a probability distribution regarding the period-ahead continuation value. The easiest and fastest way to implement stochastic dynamic programming for storage valuation is the (binomial) trinomial forest technique. With this technique one constructs a spot process in a trinomial lattice that is centered on the current forward curve and where the terminal variances emanating from each price node can be calibrated to match the terminal variance of one or another forward price process. A typical one-factor model of forward curve dynamics (see Clewlow and Strickland, 1999) is as follows: dF(t,T)/F(t,T) = σe−α (T−t) dW(t). When coupled with the assumption that the spot price converges at each termination, S(t) = F(t,t), one can establish the process for the spot price in the lattice.3 Where one previously solved the intrinsic valuation problem over two dimensions, VS(t, inv), it is now required that we add the dimension of price states, VS(t, inv, p). The lattice becomes a page for each inventory state in the problem. As before, optimization starts at the back and works towards the front, VS(0, initial inv, 1). At each node (t, inv, p) one examines each feasible inventory state change and the associated expected continuation value driven by each decision possibility; that is, each node in VS space receives a value that is now equal to max(feasible inventory change value plus expected continuation value resulting from change). Advantages of this type of storage optimization are that it is relatively easy to implement and fast to run. It can also be made quite granular over the inventory states. This helps in cases where the contract has highly nonlinear ratchets. The critical drawback of the approach is the paucity of risk factors that can be accommodated. Seasonal reversion rates may be


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accommodated, but additional tilting and twisting phenomena cannot be accommodated easily. Stochastic Dynamic Programming: Least Squares Monte Carlo The least squares Monte Carlo (LSMC) solution to storage valuation is different from the trinomial forest in two respects: permissible price processes and the calculation of continuation value. With LSMC the user employs as many risk factors in the spot process as are required to appropriately model the forward curve dynamics. If M is the number of simulated paths desired and T is the number of discrete time periods under consideration, this price matrix becomes an M ×T page (there are three dimensions in the problem; there is a two-dimensional price by time page for each inventory state) in the storage problem dimensioned over VS(t, inv, p). The calculation of expected continuation value in this technique is where the “least squares” label applies. As before, we work from the back to the front so that the terminal continuation value is zero for all inventory and price possibilities. As we move toward the front, VS(t, inv, p) is now established in the following way. ●

●

At time period t = T − 1 the valuation is performed in the usual way for every feasible inventory state. That is, VS(T – 1, inv, p) = max(feasible inventory change value plus expected continuation value associated with change), where the expected continuation value at this point is zero. Each feasible inventory state and each price now has a value. At time period t = T – 2 the following must be done for each inventory state. First, take the price at t = T – 2 for the first price path, and calculate the continuation value for that path assuming that the price for t = T – 1 is the price along that path. Calculate the continuation value using max(feasible inventory change value plus expected continuation value associated with change). Once this is done for all price paths, one will have a vector of continuation values that can be associated with the vector of prices at t = T – 2. The expected continuation value is now taken to come from

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a polynomial regression of current price states on the continuation values of the form4 CVT −1, j = b0 + b1PT −2 , j + b2 PT2−2 , j + b3 PT3−2 ,3,

●

●

where j is the price path. One can now go through each price path at t = T – 2 and check max(feasible inventory change value using PT − 2, plus expected continuation value associated with change at T – 1), which is now simply a function of PT − 2. Going through each inventory state and working one’s way back to the front, the storage value is discovered at time = 0.

The attractiveness of the LSMC approach is its ability to accommodate a large number of risk factors in the spot process. The chief drawback is the run time. If the inventory state granularity is fine, the path size M is large, and the transaction considered is of several years, the analysis might take hours to run for a single valuation. If one requires Greeks, then additional run time will be required. Spread Option Bundles The owner of storage has the right but not the obligation to inject early and withdraw later. Alternatively, if there is positive inventory, the same owner has the right to withdraw this inventory early and replace it later. By definition, the owner owns a complex portfolio of calendar spread options and forwards. This portfolio is the combination of calendar spread options and forwards that produces the best value while satisfying the constraints of the facility. In this section we describe the building blocks of storage valuation using calendar spread options. As the completed valuation is complex, we break this process into four components as follows: valuing spread options for storage; volatility term structure; linear programming; and result interpretation. Calendar Spread Options for Storage Calendar spread options are two-factor instruments that are useful in replicating the value of a number of spread-like assets. Basis spreads, transportation and transmission, and spark spreads are just a few of the assets that can be usefully replicated with these assets.


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One way of pricing and hedging these options rests on the Margrabe–Kirk analytic approximation, a geometric Brownian motion (GBM) construct that approximates the terminal spread variance at expiration. The other techniques are Monte Carlo, where the terminal spread variance is discovered via correlated random number N(0,1) draws, and what is called the double integral. In the author’s experience these valuation techniques are very close over a large number of input economies. We therefore focus on the Margrabe–Kirk analytic approximation. In the following we adopt the following conventions: b = back; f = front. We also refer to an inject-early option as a put spread option and a withdraw-early option as a call spread option. The payoff of the financially settled, European put spread option is as follows: PutSpOption = max(Fb − Ff – Strike, 0). The Margrabe-Kirk approximation for European settled spread options converts the spread into a spread return, approximates the terminal variance of this return, and then values the instrument like a Black. For the put we have pseudocode as follows: ‘Ff = front underlying ‘Fb = back underlying ‘CND = standard normal cumulative distribution F = Fb / (Ff + Strike) SpreadVol_term = (VolBack ^ 2 + (VolFront * (Ff / (Ff + Strike))) ^ 2− (2 * CC * VolBack * Vol2 * (Ff / (Ff + Strike)))) ^ 0.5 d1_term = (ln(F) + ((SpreadVol_term ^ 2) / 2) * t_term) / (SpreadVol_term * ((t_term) ^ 0.5)) d2_term = d1_term − (SpreadVol_term * ((t_term) ^ 0.5)) SprdOpt = (Ff + Strike) * (exp (−r * t_term) * (F * CND(d1_term) − CND(d2_term)) As indicated above, this formulation is for financially settled spread options. Where one wants to replicate the payoff of a physically settled

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spread option, like storage, other adjustments to the inputs are required. These are as follows. 1. If fuel is assessed under the contract for injection, then the price for the injection leg should be modified as P × (1/1 – injection fuel rate) before entering the model. 2. If fuel is assessed under the contract for withdrawal, then the price for the withdrawal leg should be modified as P × (1 – withdrawal fuel rate). 3. The strike is generally considered to be the variable charges for injection and withdrawal in dollars per million BTU. 4. For an injection-early spread option (put spread option) one should assess a cost-of-money penalty to account for the cost of funds during the time between the injection and withdrawal. 5. For a withdraw-early spread option one should assess a costof-funds benefit to the spread option. Generally, the costof-funds impact can be loaded into the front price in both cases to obtain a correct valuation. Volatility Term Structure Assuming that one is passing implied volatilities into the spread option model, a further adjustment should be made to the back leg volatility. The back leg implied volatility is only appropriate for the process that matures at the time the back leg does. In fact, the spread option will expire at the front of the front leg. Without an adjustment the back leg implied volatility will overstate the terminal variance contribution of the back leg. A simple technique for making this adjustment, used by many, is to adjust the back leg variance using mean reversion. Suppose we model the variance of the Tth contract as5

σ T2 (τ ) = σ 2 (T )e −2ατ , where τ is the annualized time to maturity, α is the speed of mean reversion, and σ2(T) is the terminal or zero maturity variance. Given this simple volatility structure, one can now restate the terminal variance as calibrated to the terminal Black variance: T T ⎡ 1 − e −2α T ⎤ σ Black (T )2 T = ∫ σ T2 (τ )dτ = ∫ σ 2 (T )e −2ατ dτ = σ 2 (T ) ⎢ ⎥. 0 0 ⎣ 2α ⎦


121

This can be solved for the terminal or zero maturity variance as follows:

σ 2 (T ) =

σ Black (T )2 T . (1 − e −2 αT ) / 2α

With a little algebra one can now derive the back leg volatility at the termination of the instrument as ⎡ e −2α ( Tb −T f ) − e −2αT ⎤ σ (T ) ⎢ ⎥ / Tf . 2α ⎢⎣ ⎥⎦ 2

OPTIMIZATION OVERVIEW Given an array of appropriately valued individual spread options, the next step is to select the highest-valued performable bundle of spread options by calling a linear programming or non-linear programming routine.6 f T x , where x is an array This linear programming routine is of the form max x of individual spread option positions and f T is an array of spread option values. There are a number of constraints. The first is an inequality constraint, Ax £ b. The constraint coefficient array times x is less than or equal to the constraint vector. The constraint vector contains constraint values for inventory, injection rate, withdrawal rate, and other boundaries of the problem. Next, we have the equality constraints Aeqx = beq. Finally, lb £ x £ ub. Any lower bound and upper bound constraint boundaries are satisfied. Once calculated, the optimal bundle of spread options can be returned as in the tables below. In Table 9.2, all possible spread options are shown, with rows representing injection periods and columns representing withdrawal periods. Table 9.3 shows a possible solution to the problem. Again, the rows represent injections, and the columns represent withdrawals.

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T A B L E

9.2

Spread option value matrix Spread Option Values

Apr 06

May 06

Jun 06

Jul 06

Aug 06

Sep 06

Oct 06

Nov 06

Dec 06

Jan 07

Feb 07

Mar 07

0.00

0.02

0.11

0.25

0.37

0.46

0.56

1.65

2.58

3.19

3.16

2.93

0.00

0.00

0.02

0.12

0.23

0.31

0.43

1.48

2.41

3.02

2.99

2.76

Jun 06

0.00

0.00

0.00

0.03

0.10

0.17

0.30

1.33

2.25

2.87

2.83

2.60

Jul 06

0.00

0.00

0.00

0.00

0.02

0.06

0.17

1.17

2.09

2.71

2.67

2.45

Aug 06

0.00

0.00

0.00

0.00

0.00

0.03

0.10

1.05

1.96

2.57

2.53

2.31

Sep 06

0.00

0.00

0.00

0.00

0.01

0.00

0.07

0.98

1.89

2.49

2.45

2.24

Oct 06

0.00

0.00

0.00

0.00

0.01

0.02

0.00

0.89

1.80

2.39

2.36

2.16

Nov 06

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.00

0.75

1.32

1.30

1.13

Dec 06

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.00

0.46

0.49

0.41

Jan 07

0.00

0.00

0.00

0.00

0.00

0.00

0.01

0.01

0.02

0.00

0.10

0.16

Feb 07

0.00

0.00

0.00

0.00

0.00

0.00

0.02

0.01

0.03

0.08

0.00

0.06

Mar 07

0.00

0.00

0.00

0.00

0.00

0.00

0.03

0.03

0.08

0.30

0.16

0.00


Apr 06 May 06


T A B L E

123

9.3

Optimal spread option values Optimal spreads

Apr 06

May 06 Jun 06

Jul 06

Aug 06

Sep 06

Apr 06 May 06 Jun 06 Jul 06 Aug 06 Sep 06 Oct 06 Nov 06 Dec 06 Jan 07 Feb 07 Mar 07

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Oct 06

Nov 06

Dec 06

Jan 07

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

3,507,198.42 1,579,481.58 0.00 585,401.58 327,918.42 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1,960,618.25 2,131,981.75 0.00 0.00 1,960,618.25 0.00 0.00 0.00

Apr 06 May 06 Jun 06 Jul 06 Aug 06 Sep 06 Oct 06 Nov 06 Dec 06 Jan 07 Feb 07 Mar 07

Feb 07

Mar 07

Solution Nuances There are a number of nuances to the spread option solutions that the user should be aware of. ●

If one aggregates deltas from the solution, the long leg of these deltas will typically be 1.5–4% longer in absolute size than the short leg. It is irrelevant whether the option considered is a put

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●

●

●

●

spread option or a call spread option. The size of this impact is a function of the volatility differentials of the two legs. The solution will be much faster than other approaches. Depending on the granularity chosen for inventory states, one should be able to generate intrinsic positions, optimal spread positions, deltas, and gammas in a couple of minutes for even a multi-year transaction using most third-party linear programming routines. In contrast, the LSMC may take hours. The trinomial forest should be faster than the LSMC but will have to trade off potentially important risk factors. With the spread option approach one gets at least as many risk factors as underlyings. In populating the return correlation coefficients used in the spread option models, one should be careful to use historical terminal correlations. Instantaneous correlations are not the appropriate drivers for the terminal variance of a spread option and can seriously undervalue spread options with long maturities. If desired, one can approximate the value of “cash to futures” by simply extending the GBM processes into the expiring front month with a continuation process and blending in a cash-tofutures correlation. Each option expiry needs to be extended by 15 days into the month as well. In selecting a model one must acknowledge that stochastic dynamic programming techniques are very much black boxes to traders. If two models give similar valuations and similar deltas and gammas, traders will prefer the spread option valuation because of their intuitiveness and similarity to instruments they might actually trade. Taking into account all the model errors appropriate for each of these models, we find few in the trading community who would prefer one of the stochastic dynamic programming models over the spread option model.

SWING OPTIONS Swing options allow their owners to either swing up or swing down over a portion of the duration of the term of the agreement. Frequently, there will be a term agreement for a specific base load daily take requirement—


125

for instance, a right for the owner to occasionally swing up or strike the option. As an illustration, the owner might be able to double his daily base load take for 10 days over the term. It is interesting that swing options in the gas business were actually far more prevalent before formal modeling of the risk of these instruments matured since the late 1990s. Prior to this swing, features were fairly commonplace in term agreements solicited by both producers and consumers. Frequently, merchants would agree to swing provisions without charge at all or with very little charge. As modeling techniques improved for understanding this option value, and as the occasional merchant lost a lot of money from giving away swing options, an understanding evolved that these options were indeed valuable. As one would expect, when these options were no longer free or cheap, fewer of them were “purchased.” General Swing Terms and Conditions Swing contracts can be formulated in a number of ways. Some of these involve simple swings, where the owner is required to pay or receive the spot price of gas for the swing quantities. Although there may be timing mismatches where some small payoff accrues to the owner of these simple swing options, these are generally viewed as physical, index swing options, and they are of no further interest here. The terms of contract structures that are of interest are as shown in Table 9.4.

T A B L E

9.4

Swing option contract terms Term

One month or multiple months

Number of strikes

Number of specific times which the owner may elect to swing; each strike requires a specific decision and notification by the owner Total cumulative quantity that can be exercised

Total quantity constraint Strike price

Strike, Strike(t) or first of month index—the strike could be a single fixed price, a vector of different strikes over the different months within a term, or it could be equal to the respective first of the month index from a publication. In all cases, derivative risk arises and must be valued

126


The similarity to storage may be obvious to the reader at this point. What one does today impacts what one can do tomorrow; it is inherently a stochastic dynamic programming problem. In a sense, it is very similar to the problem of having full inventory in storage and searching for the optimal withdrawal schedule to take the inventory to zero; that is, the swing option problem begins with a full inventory of strikes and dwindles to zero strikes at expiry. As such, it should be no surprise that the valuation can be stated in a Bellman-like equation: VSO(t, inv, TQinv ) = max [CFt ( Δinv t , P (t )) + e − rt Et ( VSO(t + 1, inv t +1 , TQinv t +1 )].

Δinvt ,ΔTQinvt

Here VSO(t, inv, TQinv) is the value of the swing evaluated at t with known remaining inventory of strikes and known remaining inventory of total quantity to be struck. If there are no total quantity constraints, this dimension will not appear in the problem. CFt is the cash flow generated at t when the spot price is P(t). VSO(t + 1) is the continuation value conditional on the forward inventory states. Analytically, we have the same tools that we had for valuing storage, with the exception that we no longer have the convenience of a spread option solution. Instead, we must use either a trinomial forest or an LSMC implementation with the same issues as before. ●

●

Trinomial forest. One-factor spot process reverting to the forward curve if required. One tree for each discrete inventory possibility (including zero). Additional dimension required for total quantity if binding. Backward recursion. Fast solution with Greeks and suitable for end-of-day processes. Least squares Monte Carlo. Multiple risk factors with reverse recursion similar to storage valuation. Tediously slow if a large number of paths are deployed.

Valuation and Hedging Nuances In the pricing and managing of swing options, there is very frequently a sizeable gap in the model’s view of optimal striking and actual striking by an owner. The model presumes financially optimal behavior.


127

In practice, owners of physical spread options acquire them for physical reliability and not financial protection. The owner may strike such an instrument even if prices are moving in the other direction. As a result, the valuation coming from our valuation models must be viewed as a boundary. ASIAN OPTIONS In this section we consider the valuation and hedging of arithmetic average price options or Asian options.7 The use of Asian options is typically motivated by a longer horizon risk tolerance. For instance, a buyer may be more concerned with average prices over a month or quarter than with an extreme price over a small number of days. The average contemplated is

∑ SA =

n

S

i =1 i

n

,

where the average is taken over the contractual averaging period. The payoff of the option is AV = max(0,h(SA − Strike)), where h = 1 for call and –1 for put. Valuation As with the other instruments discussed in this chapter, the Asian option does not have a closed-form solution. For the arithmetic average Asian option this results from the fact that when the underlying asset returns are presumed to be normal, the arithmetic average of these returns cannot be. A direct partial differential equation cannot be formed. As a result, valuation is limited to either approximations or Monte Carlo valuation. Intuitively, one would expect that if the mean and variance of the process can be suitably approximated, then one should be able to populate either the Black or generalized Black model with adjusted variables that approximate the Monte Carlo result. The question then becomes how accurate and stable the approximation is given different variable structures.

128


There are several approximations that do this in the literature. In the event one can accommodate a non-zero carry in the underlying, one can use the Turnbull and Wakeman approximation. Begin with the drift of this process. In the event one examines the instrument in advance of the averaging period and assumes a nonzero carry, the exact first moment of the arithmetic average is as follows: M1 =

e bT − e bτ , b(T − τ )

where b is the annualized carry rate, T is the original time to maturity, and τ is the annualized averaging period time. Under the stated conditions, the carry-driven price difference over the averaging period as a percentage of the time to the start of the averaging period times the carry is the exact first moment of the average price process. The appropriate carry for purposes of the generalized Black formula then becomes badj =

ln( M1 ) . T

The second moment of this average is 2 e( 2 b +σ )T 2 e( 2 b +σ )τ ⎡ 1 e b ( T −τ ) ⎤ − M2 = + ⎢ ⎥. ( b + σ 2 )( 2 b + σ 2 )(T − τ )2 b(T − τ )2 ⎣ 2 b + σ 2 b + σ 2 ⎦ 2

2

Given this variance, an equivalent Black volatility can be computed as Vadj =

ln( M 2 ) − 2 badj . T

With suitably adjusted drifts and volatilities one can now use the generalized Black–Scholes model: Call ≈ Se

( badj −r ) T2

N (d1 ) − Ke

Put ≈ Ke − rT2 N ( d2 ) − Se

− rT2

N (d 2 )

( badj −r ) T2

N ( d1 ).


129

SWAPTIONS The European swaption allows the owner to enter into a swap at the maturity of the option. If the owner has a call on the swap—a payer swaption—then the payoff at termination is ⎛ ∑ dfiQi Fi ⎞ VSwpn = max ⎜ i − Strike, 0⎟ , ⎝ ∑ i dfiQi ⎠ where dfi is the discount factor for the ith forward included in the swap contract to time zero, the termination of the swap, and Q,F are quantities and forwards for ith components of the swap. To value this option for general t, one requires a process either for the swap rate itself or for correlated components of the swap rate. In markets where swaptions are liquidly traded (interest rates, some power markets) the swap rate or the swap rate return itself is frequently modeled as lognormal for standardized swap terms. With this liquidity an implied volatility is discoverable, and the instrument can be priced using standard Black76 models. Where swaptions are traded less frequently, as in the case of natural gas, it is more common to model the correlated components of the swap directly and value the option using Monte Carlo simulation. The following Matlab code shows how this might be accomplished. Assume that F0 is a 1 × N array of initial forwards that are components of the swap and that T is the termination of the swap. Define AdjVol as the Trajectories × N array of volatilities that expand correctly with the square root of time but expire at T. For instance, the ith component in the swap can be associated with an implied volatility that is suitable for expansion to its termination. However, the swaption expires at T, earlier than the ith swap component. The implied volatility must therefore be adjusted using mean reversion or some other technique so that it correctly maps to the terminal variance of this ith component at T, when the swaption expires. Finally, define corrRandn as the Trajectories × N matrix of correlated random numbers drawn from N(0,1). Note that this array cannot be drawn from Cholesky factorization. What is required is the terminal correlation of the asset returns at T, not the instantaneous correlation. As a result, there is no particular reason why this matrix should be positive definite, a requirement for Cholesky factorization.8

130


F = repmat(F0, [Trajectories,1]).*exp(−.5*T*AdjVol.^2 + sqrt(T)*AdjVol.*corrRandn); %Monte-Carlo estimate Call = exp(−r*T)*mean(max(F*w’−Strike,0)); Put = exp(−r*T)*mean(max(Strike−F*w’,0)); In general, the structures used to generate the adjusted volatilities for swaptions should be consistent with the adjustments made for calendar spread options. Market quotes for either instrument should help the user in maintaining a fresh volatility structure. REFERENCES Clewlow, L and Strickland, C (1998) Implementing Derivative Models (Chichester: Wiley). Clewlow, L and Strickland, C (1999) Valuing energy options in a one factor model fitted to forward prices. Quantitative Finance Research Center Paper, University of Technology, Sydney. Clewlow, L and Strickland, C (2000) Energy Derivatives: Pricing and Risk Management (London: Lacima Publications). Eydeland, A and Wolyniec, K (2003) Energy and Power Risk Management (Hoboken, NJ: Wiley). Haug, E. (1997) The Complete Guide to Option Pricing Formulas (New York: McGraw-Hill). Jaillet, P, Ronn, E, and Tompaidis, S, (2004), Valuation of commodity-based swing options, Management Science, 50(7), pp. 909–21. Jarvinen, S and Toivonen, H (2004) Pricing European commodity swaptions, Applied Economics Letters, 11, pp. 925–9. Lari-Lavassani, A, Simchi, M, and Ware, A. (2001) A discrete valuation of swing options, Canadian Applied Mathematics Quarterly, 9(1), pp. 35–74. Turnbull, SM and Wakeman, LM (1991) A quick algorithm for pricing European average options, Journal of Financial and Quantitative Analysis, 26, pp. 377–89.

NOTES 1. It is specifically not the goal of this chapter to proliferate mathematical notation. We therefore use the notation in the references cited where possible. Any errors in this are the author’s. 2. Of course, the initial inventory need not be zero at the beginning of the analysis. It should be whatever the initial inventory happens to be at that time. 3. Several of the references to this chapter show graphically how the trinomial forest is implemented.


131

4. The polynomial regression with more or fewer variables is frequently good. Others have experimented with different nonlinear forms with varying results. The developer should, of course, experiment. 5. There are numerous parameterizations one can deploy in fashioning a deterministic volatility function; this is one of the simplest. 6. The optimization problem has an objective function that is linear and convex in spread option prices. The problem may suffer non-linear constraints, though, if ratchets are highly non-linear. In these cases one can often get good approximations by using Lagrangian relaxation techniques. 7. Geometric average rate options are also frequently referenced in the literature. Assuming the underlying follows a lognormal process, the geometric average rate option has a closed-form solution. However, the interest for protection of geometric averages in energy markets is trivial, and we will not further consider it here. 8. Given the terminal correlation, one can use Box–Müller transformation to generate correlated Gaussian random number


C H A P T E R

10

Liquidity Risk Measurement and Management for Energy Firms Carlos Blanco, Kevin Dowd, Kevin Kremke, and Robert Mark

INTRODUCTION The management of liquidity risk is an increasingly important concern for energy and commodity trading firms. In this chapter we introduce the main components of a proactive liquidity risk management framework covering policy, methodology, and infrastructure issues. We illustrate the framework components with several examples from energy and commodity firms. We also review Standard & Poor’s liquidity adequacy guidelines for energy trading firms in the context of the liquidity framework introduced in the chapter. COMPONENTS OF A LIQUIDITY RISK FRAMEWORK A liquidity risk management framework can be defined in terms of a policies-methodologies-infrastructure (PIM) framework (see Figure 10.1). The components of this framework are: ●

●

Policies and procedures. A clearly articulated liquidity risk strategy with written policies and procedures, with clear lines of authority and effective disclosure. Methodologies. Metrics to measure and monitor liquidity risk, including stress tests and metrics for performance measurement. 133

134


F I G U R E

10.1

Policies, methodologies, and infrastructure framework

●

Infrastructure. Having in place the right people with access to the right information in a timely fashion.

Liquidity Risk Policies and Procedures Liquidity risk policy needs to address the potential for crises that can stem from external market conditions or structural problems. For example, internal credit portfolio problems can precipitate a liquidity crisis that is then exacerbated by weaknesses in an energy firm’s funding strategy. The collapse of Enron is a good case in point. Like most decisions, liquidity risk decisions are driven by trade-offs, and these are usually trade-offs between cost and risk. A key challenge of managing liquidity risk therefore lies in setting policies that optimize the funding capacity of the energy firm in the face of their profit objectives. Broadly speaking, liquidity risk policy should address how much liquidity risk the company is willing to accept, identify trade-offs, and put into context the results of the firm’s liquidity risk modeling techniques. Liquidity risk management should not be mistaken solely for “crisis management,” even though it is commonly the case that lack of planning often forces companies to address liquidity risk management issues only when crises occur. For instance, it should be self-evident that firms should secure adequate access to credit lines before a stress event takes place. In doing so, a key goal is to avoid concentration of debt payments, particularly in short-term maturities. It is also important to maintain cordial relations with banks and other suppliers of capital while simultaneously aiming to

CHAPTER 10 Liquidity Risk Measurement and Management for Energy Firms

135

avoid excessive dependency on external sources of financing. Firms should bear in mind that some “committed” lines of credit might not be honored in times of stress, and in such circumstances a firm may have limited ability to secure new funds at short notice. A liquidity risk strategy also requires a set of procedures that set out operating standards. These procedures should be goal-congruent with the business strategy and cover both funding liquidity and trading liquidity risk. Among the issues to be addressed are the following: ●

●

●

a delegation of authority matrix to establish how much decisionmaking power is granted to various levels of management, including authority over position limits; escalation procedures to give senior management timely notification of limit breaches; the actual quantitative metrics to be used.

The policy should also outline contingency plans as well as the role and authority of the liquidity management team in the event of a crisis. Contingency plans should detail strategies to handle unexpected events that might severely strain the firm’s liquidity and also ensure that the firm gets secure access to cash under such circumstances. A key building block of best-practice liquidity management is to disclose liquidity-related risks promptly and effectively. This disclosure should be both internal (i.e. to other parties within the firm) and external (i.e. to external stakeholders). Good disclosure promotes confidence in the firm and enhances its credibility. In Figure 9.2 we can see a forward-looking disclosure suggested by the Committee of Chief Risk Officers (CCRO) of the expected liquidity supply and demand conditions 12 months forward. The disclosure is broken down by the largest supply and demand components, and the chart reports the likely capital surplus/deficit. This information allows interested parties to evaluate the liquidity or cash flow health of the firm. Methodologies and Metrics The major sources of liquidity risk can also affect the firm’s exposures to market risk, credit risk, and operative risk (defined as the sum of operational and operations risk). These risks are often interrelated, and therefore modeling of market, credit, and operative events should be done jointly in order to understand a firm’s overall liquidity risk profile.

136


B O X

10.1

Types of liquidity risk There are two main types of liquidity risk. Funding liquidity risk is the risk arising from not being able to meet collateral and margin calls, not being able to honor cash obligations, and not being able to access the capital markets or other sources of finance when funds are needed. A good recent example is the case of China Aviation Oil Singapore, which experienced a $550 million loss that arose when the firm was unable to meet collateral and margin calls. Trading liquidity risk is the risk of not being able to exit a trading position without incurring significant costs. The classic example was in 1993, when Metallgesellschaft AG (MG) lost $1.4 billion after a failed hedging strategy put together by one of its traders (commonly referred to by his colleagues as a “cowboy without cattle”). The hedging strategy consisted of building large exposures in long-term forward contracts, which were hedged by regularly “rolling over” futures contracts. This hedging strategy was poorly designed because it failed to take account of the very large liquidity risks it created. Large margin requirements then put unbearable pressure on MG’s liquidity and led to the company liquidating its hedges at a massive loss. This phenomenon is commonly referred to as “hedger’s ruin”: futures contracts are used to hedge a long-term asset that is not marked to market, and the cash required to meet margin calls can be so large that the entire position must be prematurely liquidated at huge cost.

Introduction to Liquidity Risk Modeling: Cash Flow at Risk (CFaR) and Liquidity at Risk (LaR) A best-practices liquidity risk management framework rests on an appropriate system of metrics that can provide insights into the liquidity position

F I G U R E

10.2

Example of forward-looking liquidity adequacy disclosure

Source: CCRO (2003)


137

of the firm at any time and identify scenarios that could result in liquidity shortfalls. In order to estimate liquidity risk, we need a framework that explicitly focuses on the potential demand and supply of cash. This framework should address medium- and long-term horizons as well as the cost of liquidating positions in a stressed market environment. The horizon choice should also take into account the expected “unwinding” or “neutralization” period for the different instruments and assets in the portfolio. Box 10.2 compares LaR to VaR. The modeling framework must also take account of the complexities involved with liquidity, particularly for long horizons, and the number of risk factors involved. One-step simulations tend to over-simplify both market events and portfolio responses to those events. We can think of modeling liquidity risk as the equivalent of modeling a path-dependent option, such as a barrier or digital option, where insolvency results if the barrier is hit at any point during the period being modeled. In addition, the modeling must also take account of dynamic portfolio management or the way in which the institution responds to events (through stop-loss triggers, VaR-based limits, etc.). In many ways the ideal is a multi-period dynamic framework in which portfolio positions are allowed to evolve dynamically as scenarios unfold. This would take account of pre-specified trading strategies (stop-loss, delta-neutral, dynamic hedging, taking positions until expiration, etc.) and the ways in which prices and the amounts traded depend on the market liquidity. This type of framework could also take account of B O X

10.2

How big is LaR compared to VaR? It depends Let us assume that we have a large market-risk position that we hedge with a futures contract that closely follows the behavior of the underlying exposure. The futures hedge leaves us exposed to the possibility of margin calls, and our exposure to margin calls will be related to the size of the futures position, which in turn corresponds to the gross size of our original position. In this case the value at risk (VaR) depends largely on the netted or hedged position, while the LaR depends on the larger gross position. If the hedge is a good one, the basis risk (or the VaR) will be low relative to the gross risk of the hedge position (or the LaR). In this case the LaR can easily be an order of magnitude greater than the VaR. On the other hand, there are also many market risk positions that have little or no cash flow risk (e.g. a portfolio of long European option positions, which generates no cash flows until the position is sold or the options expire), and in such cases the VaR will dwarf the LaR. In short, depending on the circumstances, the LaR can be much greater than the VaR or much less than it.

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important features of liquidity crises, such as large drops in asset prices, large increases in volatilities, and crisis-driven changes to bid-ask spreads. It is also important to choose the right variables to model (e.g. markto-market (MtM), earnings, or cash flows) to identify the most material risk factors (e.g. spot versus forward prices, implied volatilities, volume, counterparty risk, rating downgrades, etc.) to use appropriate models for tail risks (e.g. power-law tail or extreme-value approaches) and to use superior risk measures (i.e. coherent risk measures such as the expected tail loss (ETL), rather than the VaR). Stress Tests A well-designed set of tests can provide powerful insights into potential exposure and associated liquidity problems under periods of market stress (see Figure 10.3). These should work to combine market, credit, and operational events and also attempt to model other participants’ behavior under stress circumstances. CFaR stress models focus on the potential demands on cash versus sources of cash available at any point in time. Such models identify the

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10.3

Scenario analysis for liquidity risk


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key drivers that could increase the demand for cash relative to the available sources. Potential outcomes could be identified, and these could be probability-weighted by the modelers. CFaR and LaR metrics can then be calculated based on the results for these scenarios. For each scenario: 1. Stress or simulate market prices at multiple horizons using a realistic model of the evolution of the spot price and forward price curves. As far as possible, the price processes should be consistent with those from market and credit risk simulations, but the underlying assumptions about the price process should also be as realistic as possible. So, for example, if we are modeling electricity or natural gas price behavior, price spikes should be incorporated in the models, even if market or credit risk models are based on other distributions. 2. Calculate MtM by counterparty, legal entity, or contract after taking into consideration netting agreements and other contract rules. 3. Stress or simulate possible contingencies that could “trigger” collateral and margin calls or impact the financial liquidity of the firm. Those events could range from a possible company credit downgrade below investment grade to counterparty defaults, operational events that could impact the financial health of the firm, etc. 4. Stress or simulate operative events (e.g. plant outage or pipeline blowup) that could have a material impact on the liquidity of the firm. 5. Account for trading rules and management’s intervention if certain thresholds (stop losses, VaR limits, etc.) are breached. 6. Determine cash inflows and outflows for each time step after taking into account fixed payments and contingent liquidity claims due to market and credit events. Monitoring and Validating Liquidity Risk Models The last building element in the model and methodologies component of the framework is the regular validation and back testing of the models used. The firm can monitor trends in terms of liquidity use versus forecasts in order to diagnose discrepancies and correct for them. This is a critical step in the modeling process in that it helps to validate or refute some of

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the underlying assumptions. Where there is divergence between the actual and modeled liquidity requirements, the model needs to be adjusted and the underlying assumptions validated. Regarding trading liquidity risk, firms can collect information on bid–ask spread charges versus expected values and monitor difficulties in closing down certain positions as well as evaluate alternative instruments (e.g. futures contracts) and markets (e.g. highly correlated commodities) to hedge some of the risks in illiquid positions. Such information can help to calibrate models and identify the liquidity consequences of particular types of events. Funding liquidity processes should also be regularly evaluated as well as reviewed with the board, and these should also take into account the contingency plans developed for extreme circumstances. The following box shows the S&P guidelines for liquidity adequacy. INFRASTRUCTURE In the event of a liquidity crisis it is essential to have access to near-real-time information on current and expected major liquidity inflows and outflows. This in turn requires that management information systems should produce timely, accurate, complete, and meaningful information on all aspects of the firm’s liquidity. The company’s systems should allow risk managers to conduct stress tests on liquidity, and these should take into account both material contract clauses and aggregation rules by counterparty. Ideally, they should also allow for a pre-deal check against counterparty limits and provide an indication of the current and potential impact of trades on available liquidity. It is important to keep in mind that certain trades, particularly futures hedges, can severely drain the short-term liquidity of a firm. A key element of an effective infrastructure is a formal line of communication between traders, managers responsible for funding activities, and senior management. Traders have a keen eye on the market and may be able to anticipate additional liquidity needs as positions change and prices fluctuate. For their part, senior management has insight into macro-events and corporate considerations that can impact the available liquidity. The infrastructure should also create incentives to align commercial behavior with the overall liquidity profile of the company. For example, a simple plan incorporating liquidity costs into trader compensation can result in traders considering the long-term liquidity implications when analyzing deals with different tenors and collateral call provisions.


B O X

10.3

S&P guidelines for liquidity adequacy S&P’s liquidity adequacy guidelines focus on a company’s liquidity under stress scenarios that combine a crisis of confidence in the firm’s financial condition with large market moves. S&P indicates that these benchmark measures would allow them to establish minimum guidelines to enhance analytics and expedite comparisons between firms. S&P also clearly states that a shortfall from its guidelines could cause a rating downgrade. S&P recommends that energy firms should stress test their portfolio based on market-related events (such as a 30 percent shift in the next months in the forward curve and 20 percent in subsequent months—see Figure 10.4) and credit events (such as a company downgrade to below investment grade that triggers collateral calls for all negative MtM positions). The two key ratios analyzed by S&P are: ●

Credit event liquidity adequacy (CELA) = Primary liquidity/ (negative current MtM + other liquidity calls). ● Market and credit risk liquidity adequacy (MCELA) = Primary liquidity (negative MtM with stress test and other liquidity calls). Primary liquidity is defined as unrestricted cash on hand and availability under credit facilities, plus cash and letters of credit already posted to offset negative MtM exposures. However, secondary liquidity is also taken into account and includes a company’s free cash flow, cash generated from selling inventory and receivables and uncommitted credit lines. Cash held as collateral is not considered as primary or secondary liquidity, and parent guarantees are only considered as liquidity under certain provisions. Negative MtM and collateral exposures depend on the type of collateral triggers (e.g. hard versus soft) in the event of a credit downgrade expressed in the trading agreement. Table 10.1 presents a summary of margin and collateral arrangements common in energy and commodity trading contracts.

F I G U R E

10.4

Stress shocks for forward curves: NYMEX natural gas (September 2, 2005)

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T A B L E

10.1

Types of collateral and margin payments as a function of the creditworthiness of the counterparty Collateral

Description

100% MtM

Based on negative, “under-the-water” or “out-of-the-money” MtM To account for the high volatility and potential changes in exposure Fixed amount or variable amount based on potential credit exposure or credit-VaR numbers Variable collateral based on the credit rating. For strong credit ratings, the collateral may be below 100% of negative MtM, while for weakly rated counterparties, the collateral may be higher than 100%

>100% Risk-based margin Credit-rating-based margin ladder

Example 10.1: The US Merchant Energy Market after the Enron Debacle The collapse of Enron in 2001 led to a liquidity crisis in the merchant energy market. Credit ratings plummeted and invoked material adverse change contract provisions that allowed bilateral counterparties to call collateral immediately. This effect was also compounded by the more onerous collateral provisions imposed by the NYMEX for positions already in place. Many of these positions were in place to hedge offsetting, lowfungibility power generation, and retail energy contracts that would be difficult, expensive, and/or time-consuming to unwind. To add to the difficulties, many potential buyers of these contracts were experiencing similar difficulties and were not in the financial position to take assignment. The result was a classic example of the hedger’s ruin dilemma, in which futures contracts are used to hedge a long-term asset that is not marked to market. From a total value perspective the position appears to be effectively hedged, but the necessary cash to meet margin calls can be so overwhelmingly large that the position must be liquidated at a large loss (see the Box 10.3). This situation required immediate crisis management and implementation of a liquidity contingency plan. However, many firms were caught by surprise and were quite unprepared for the crisis when it came. As a result, many suffered large losses that could have been avoided.


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CONCLUSIONS Sound liquidity risk management is a key factor in maintaining a firm’s financial health. One of the most topical issues in energy and commodity markets at the moment is how to design and implement a liquidity adequacy framework. Market participants, credit rating agencies, regulators, and other stakeholders are pushing energy merchants to establish such frameworks in order to restore confidence in their ability to meet their financial obligations. Proactive liquidity risk management is the only alternative to “crisis management” in the event of an exogenous or endogenous liquidity crisis. REFERENCES Blanco, C and Mark, R (2004). EWRM for energy trading firms: EWRM starts with risk literacy, Commodities Now (September). Committee of Chief Risk Officers (2003) Emerging practices for assessing capital adequacy. White Paper. Crouhy, M, Galai, D, and Mark, R (2000) Risk Management (New York: McGraw Hill). Dowd, K (2005) Measuring Market Risk, 2nd edition (Chichester: Wiley). Hsieh, T and Spangler A (2004) Analyzing the liquidity adequacy of U.S. trading and marketing operations, Ratings Direct (May 4). Venkataraman, S (2003) Liquidity Provisions in US Energy Trading Contracts (New York: Power Marketers Industry Publications, November 14).


C H A P T E R

11

Value of Technical Analysis in Energy Markets Tom James

INTRODUCTION Timing is the key to any successful trading or hedging program, but getting the timing right will always be more of an art than an exact science. However, there are some tools that can build a clearer picture of when the market price trend may change, which in turn should provide an idea of market direction and timing. There are two main types of analysis that can be carried out: fundamental analysis and technical analysis. Fundamental analysis deals with the supply and demand factors of the physical energy world, whereas technical analysis is concerned with the price history of the market. In reality, most people use a combination of the two—what could be termed “technofundamental” analysis. In other words, when a general technical picture of market direction and timing has been established, any new fundamental information can be incorporated into the picture as it is announced. If a trader starts with a clear technical picture, then whenever news or information comes into the market during the trading day, he or she should ask three key questions: Is this new news? Is this fresh news? Has the market already seen this? The last question is important because sometimes information or events are rumored in the market, and, as the saying goes, people “buy the rumor and sell the news.” In energy markets this can often lead to a situation in which the market will fall on bullish news or 145

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rise on bearish news. In such instances, the news or information is already accounted for in the price. The confirmation of the news gives the signal for people speculating on the rumor to take their profit and close out their position. This chapter presents the key technical approaches and tools that work well together when applied to major energy futures markets. Note the word “together”—technical analysis is a bit like detective work in that it requires ongoing attention to all evidence that might support any theory on the direction of price trend and the timing of market entry and exit. WHAT IS TECHNICAL ANALYSIS? Figure 11.1 is a typical futures bar chart. Each bar represents one time period, in this case one trading day. The line on the left-hand side of the bar represents where the market opened (the first traded price of that trading day); the top of the bar is the high of the day, and the lowest level of the bar is the low of the day. The line on the right-hand side of the bar represents the closing price (the last traded price or official settlement price). The arithmetic scale is the most popular one used for bar chart construction showing price and time. The logarithmic scale is of little use F I G U R E

11.1

Futures bar chart A bar chart displays a security’s open (if available), high, low, and closing prices. Bar charts are the most popular type of security chart

Source: Tom James

CHAPTER 11 Value of Technical Analysis in Energy Markets

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for technical analysis, although it can be useful for bringing different commodities down to the same scale for analyzing which markets are taking the lead in percentage terms. There are a number of ways of defining technical analysis, but in a nutshell, it is the study of market prices, with price charts being the primary tool (see Figure 11.1). It is based on the idea that historical price movements of a commodity can be used to predict the sentiment and the expectations of market participants with regard to the future price trends. Another way of looking at technical analysis is to see it as applied social psychology. That is because it sets out to recognize trends and changes in crowd behavior. In many ways, technical analysis is all about trying to predict what the majority of traders believe will happen next in terms of the price direction of the market. In fact, one of the main reasons why technical analysis works is simply that everyone believes it works. The majority of people trading in the markets are influenced by technical analysis, and so its predictions can be, to some extent, self-fulfilling. It therefore follows that we must examine the key technical analysis tools that the majority will be basing their decisions on. One thing is certain: technical analysis can help when making timing and market direction predictions. However, it is not enough to rely on a single technical tool; a combination of five or six technical tools and approaches is usually needed to help build up a good picture of market trend price targets and timing. It should also be remembered that there are certain types of market price movement that can render some technical analysis tools useless and too unreliable to follow. The key here is to recognize when technical analysis tools should be treated with caution. THE PRINCIPLES OF TECHNICAL ANALYSIS Technical analysis works on some key principles. These are as follows. ●

●

All known market fundamentals (news in the market) are accounted for and reflected in market prices. The market has absorbed all the news, and the price represents a consensus on where price should be based on all known data. This is certainly true in efficient markets that have good trading volume (liquidity). Prices move in trends, and trends persist.

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● ●

Market action is repetitive or cyclical. If we accept the fact that human emotions and expectations play a role in commodity pricing, we should also admit that our emotions play a role in our decision-making.

The key rule for anyone looking to use technical analysis is to keep it simple. The user should go with technical analysis tools and approaches that most of the world will be looking at. After all, you are trying to predict what the majority of traders are thinking. So it is important to watch the tools that they will be looking at, which in turn will affect their own perspectives on future price trend to some extent. It is also useful to refer to news agency reports on the market as these often discuss technical analysis tools on the market. You can be sure that these tools will have some bearing on perceptions of future trends. The roots of modern-day technical analysis are in the Dow theory, developed around 1900 by Charles Dow. Stemming either directly or indirectly from the Dow theory are principles such as the trending nature of prices, prices discounting all known information, volume mirroring changes in price, and support and resistance. Of course, the widely followed Dow Jones Industrial Average is a direct offspring of the Dow theory. TRENDLINES Before getting into any mathematical analysis calculations, there is much information and guidance on future price movements that we can extract from the basic open, high, low, close of a price chart (Figure 11.2). The concept of trend is essential to this approach to technical analysis. Generally, the trend is simply the direction of the market. More precisely, market moves are usually a series of zigzags, resembling a series of waves with fairly obvious peaks and troughs. It is the overall direction of these peaks and troughs that constitute market trend. Most of the time, traders watch for a change in trend and subsequent confirmations that the trend is changing or has changed before acting on that information. Trendlines play an important part in illustrating that a change has been made and also give traders an indication of the price levels that might trigger a price change or a new buying or selling interest. Trendlines should be drawn off two price points—a high or low and the earliest price points that can be found. The trendline should then be confirmed by a third test, as illustrated in Figure 11.3.


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11.2

Bar chart of prices A bar chart displays a derivative’s open (O) (if available), high (H), low (L), and closing (C) prices. Bar charts are the most popular type of technical chart

Source: Tom James

F I G U R E

11.3

Trendline chart Draw trendline initially from points 1 and 2, then the market has made this potential trendline a valid line to follow after holding support at point 3

Source: © FutureSource UK Inc

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Figure 11.4 shows an example of a bull trend, while Figure 11.5 shows a bearish trend. TRENDLINE AND BREAKOUT A breakout is where a trendline is finally broken, indicating that if good trading volume is seen at the same time, a change in price trend could be taking place. Other indicators help to identify when a trend change may take place, which is usually followed by a break in the trendline, giving confirmation of a trend change. Support and Resistance Alongside trendlines on charts, clear patterns of support and resistance can also be spotted. If the energy price is thought of as an ongoing war between the bull (the buyer) and the bear (the seller), then support and resistance levels can be seen as the battlefields in that war. In other words, support and resistance levels represent barriers to change. A good way to quantify expectations following a breakout from a trendline or from resistance or support levels is to look at the volume associated with the price breakout. If prices break through the support/resistance level with a large increase in volume and the move-back is on relatively low volume (resistance becomes support), it implies that the new expectations will rule (a minority of traders are unconvinced). Conversely, if the breakout is on moderate volume and the move-back period is on increased volume, it implies that very few traders’ expectations have changed, and a return to the original expectations (i.e., original price trend) could be seen. This can be seen in Figure 11.6. Low volume levels are characteristic of indecision (if there are no major international holidays at the time) or an expectation of possible change. This typically occurs during price consolidation periods—periods when prices move sideways in a trading range. Low volume also often occurs in the indecisive period during market bottoms or tops. Sometimes traders and brokers will refer to the market “bottoming out” or “looking toppy.” This means the market may reverse its previous trend. On the other hand, high volume levels are characteristic of market tops when there is a strong consensus that prices will move higher. High volume levels are also very common at the beginning of new trends (i.e. when prices break out of a trading range). For example, just before market bottoms, volume will often increase due to panic-driven selling.


F I G U R E

11.4

Example of uptrend or bull trend


F I G U R E

11.5

Downtrend or bearish trend Here the market is hanging around the support trendline but does not close below the trendline, and volume did not increase


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F I G U R E

11.6

Example of support and resistance chart

Source: FutureSource UK Inc.

Volume can also help determine the health of an existing trend by indicating whether it is strong or weakening. A healthy up trend should have higher volume on the upward legs of the trend and lower volume on the downward (corrective) legs. A healthy down trend usually has higher volume on the downward legs of the trend and lower volume on the upward (corrective) legs. OTHER TYPES OF CHARTS Candlestick Charts In the 1600s the Japanese developed a method of technical analysis to analyze the price of rice contracts. This technique is called candlestick charting. Candlestick charts display the open, high, low, and closing prices in a format similar to a bar chart but in a way that highlights the relationship between the opening and closing prices (Figure 11.7). Candlestick charts are simply another way of looking at prices that do not involve any calculations. They have their uses, especially for traders in


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11.7

Candlestick chart

markets such as bonds, but in the energy markets, there is only one key formation that is worth looking out for as it can give an early warning of a major trend change. This is the “DOJI” formation, as illustrated in Figure 11.8. I have noticed this DOJI formation on three or four occasions over the past seven years, and when it has shown up in NYMEX or IPE futures contracts, it has been followed by a trendline support break, and in one instance the market dropped some US$3 dollars a barrel on IPE Brent in just a few days. The VIP Relationship (Volume, Open Interest, and Price) It is possible to build up a good picture of what the market is thinking from a combination of trendline analysis (using charts, support and resistance levels), volume (using the total market volume), and open interest information. Volume is a simple but key aid when analyzing the market. It can give a good real-time signal as to the level of interest in a new trend starting or an old trend finishing. Volume, combined with open interest

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11.8

DOJI formation

(which is available in futures markets contracts but not equities), gives a very good combination tool to analyze whether a trend could continue or may be coming to an end. Open interest is the net number of futures or option contracts in existence on an exchange, counting a bought contract and a sold contract as one open contract, or a figure of one in open interest terms. Another example of the VIP relationship is shown in Figure 11.9. In this example, the VIP relationship shows the following. 1. The market is moving higher, volume is increasing, and open interest is increasing. This shows that the bullish uptrend is well supported, with new buyers coming into the market. 2. The market is moving higher, but volume and open interest are decreasing. This relationship shows that there is no new interest in continuing the bullish trend and in fact, with open interest decreasing, the market looks like it is closing out of (selling out of) previously bought (long) positions which could be showing a profit. This market trend is showing signs of weakness, so watch out for a change in direction. 3. The market changes direction in this example and starts moving lower. Volume and open interest both increase, illustrating new selling interest coming into the market, which in turn supports the bearish trend.


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11.9

VIP relationship

Source: Tom James

4. In this part of the market trend example, the market is moving lower still, but on lower volume, and also open interest is reducing, showing that some players are slowly losing confidence in the current trend continuing. As a result, they are buying back previously sold positions, taking profits, and closing out their positions, which is reducing open interest.

END OF TREND SIGNAL The end of a trend is often signaled when volume becomes progressively smaller and smaller each trading day, and the price range of trading days (the distance between the high and low prices of the day) is also reduced. The period between one trend nearing its end and a new trend starting can be a time when the market is waiting to make a decision on a new trend. The decision is made once a trendline is broken or key support or resistance triggers renewed trading interest with increased volume. It is also worth emphasizing that a significant increase in volume should always be seen when a trendline or a key support/resistance level is finally broken. If not, then it may be a false break-out. This can sometimes occur when markets are very quiet. Some speculators may be tempted to force the market on low volume through a well-publicized trendline level in an attempt to trigger some reaction in the market.

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11.10

Example of price gaps

Source: Tom James

Another good way of determining where the market may head is to look out for price gaps on price charts (Figure 11.10). Energy futures markets often use price gaps as targets. A question many traders ask is how far back in time they should look for price gaps. I have found that on daily bar charts (where each bar represents one trading day) you can often see that price gaps that have occurred as much as three months in the past are still watched by the market. But price gaps are not just indicators of price targets. They can also indicate whether an old trend is going to start again. This can be seen in the case of a bullish trend, when a market breaks support and then comes down to aim for a price gap. If the market fills the price gap and holds the bottom of the gap (as illustrated in Figure 11.11), then it can be expected that buyers will come back and that the bullish trend will have a lease of new life.


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11.11

Example of price gap

Source: Tom James

In the example of Figure 11.11 the market has been in a bullish trend, but corrects downward. The price gap is filled, but the market holds the bottom of the gap to continue the original bullish trend. In a case like this, renewed buying interest can normally be seen. In Figure 11.12 the market has been in a bearish trend and corrects upward. The price gap is filled, but the market holds the top of the gap to continue the original bearish trend. In this case, renewed selling pressure can normally be seen. FIBONACCI RETRACEMENT LEVELS So far, we have looked at trendlines which can help to identify trends and establish key support or resistance levels which can highlight a trend being broken. We have seen how volume data, open interest data, and price data (VIP relationship) can give early warnings of trends coming to an end and how much interest there is from market participants to help continue the current trend. We have also looked at price gaps as price targets that give traders an idea of how much the market might move in a particular direction. Another good way to predict price targets is by using Fibonacci percentage retracement. F I G U R E

11.12

Another example of price gap

Source: Tom James

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Fibonacci was the nickname of the thirteenth-century mathematician, Leonardo Pisano, who (re)discovered what is today known as the Fibonacci sequence. This sequence begins 1, 1, and then subsequent terms are calculated by summing the previous two terms: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55. . . . Ratios of these numbers to each other give us important values: 61.8%, 50%, and 38.2%. The prevalence of these ratios can be found all around us—from the double helix of DNA to spiral galaxies. The pioneering work of traders like W. D. Gann (Gann lines) and R. N. Elliott (Elliott wave theory) also showed that these ratios are prevalent in the financial markets. When properly applied to energy futures markets, they are surprisingly reliable, and the market watches these retracement levels avidly. Energy futures markets (e.g. IPE, NYMEX) tend to reverse or consolidate once they reach one of these ratio levels (measured from the distance of the previous trend reversal). This means that they can be very useful as position entry and exit levels. There is a tendency for the energy futures markets to retrace down (during a bullish trend) or recover (during a bearish trend) by 50% before continuing the original trend. In the example given in Figure 11.13 the

F I G U R E

Retracement

Source: Tom James

11.13


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market retraces 50%, then carries on the original bullish trend. Besides this 50% retracement, there are minimum and maximum retracements that should be allowed for: 38.2% and 61.8%, respectively. What this means is that in a correction of a very strong trend, the market may only retrace 38.2% of the previous move. If a trader is looking for a buying or selling opportunity (depending on the trend), the trader can compute the Fibonacci retracement levels and use them as a reference point to enter or exit the market. CHART READING To identify Fibonacci retracement levels, the most recent highest point and lowest point in the futures chart should be identified. Once this is done, you are ready to measure the retracement percentages. Most energy futures contracts, after making long sustained moves in one direction, will eventually retrace a portion of the move before continuing on to extend it. Most commercially sold stock-charting software packages will automatically draw in Fibonacci levels between short-, medium-, and long-term pivot points using traditional 38.2%, 50%, 61.8%, and 100% retracement levels. These price levels can be watched as price targets or resistance points when selling (profit taking on long positions) or when calculating levels in the opposite (downward) direction. Price targets as support points where short covering (buying back) may occur and fresh buying interest should come into the market. For technical analysis the important thing is that in the oil markets, people follow Fibonacci percentage retracements, and in fact, these work so well that sometimes the market has been seen to touch the Fibonacci target level exactly and then hold and recover its trend. MATHEMATICAL INDICATORS There are many types of mathematical indicators in the technical analysis field, but here we focus on some key ones that work on a consistent basis for the energy futures markets. These indicators can give a trader a simple yet very effective tool for building up a view on price direction and timing when used in parallel with bar charts, support/resistance levels, gaps, trendlines, volume, and open interest information.

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The Relative Strength Index The easiest way to describe how the relative strength index (RSI) reflects the market is to say that the RSI treats the futures market price as if it were a rubber band. The rubber band can be stretched just so far, but after a certain point, unless it breaks, it is forced to contract. The idea was developed by Welles Wilder (1978). The RSI is relatively straightforward to calculate. Denote the average of the closes of the up days by U and the average of the closes of the down days by D. Then the index is given by RSI = 100 −

100 . 1+U / D

The time frame specified determines the volatility of the indicator. A lot of technical analysis books and even news reports talk about a 9-period, 14-period, or 21-period time span for analysis. These time periods are usually applied most effectively to daily bar charts; however, there is nothing to stop you from applying the RSI to longer or shorter time frames. It is probably a good idea to use two RSIs rather than one. Using one short time period and one longer time period can help a trader to assess how much an energy futures market is overbought or oversold. (For energy futures a 3-day RSI and a 14-day RSI are recommended.) “Overbought” means that the market price has moved higher too quickly in the time period under analysis on the RSI, while “oversold” means that the market price has moved lower too quickly in the period. Let us apply the formula to show how to carry out a 14-day RSI calculation. 1. Add the closing values for the up days and divide this total by 14. This gives U. 2. Add the closing values for the down days and divide this total by 14. This gives D. 3. Divide U by D. U/D is often referred to as the relative strength (RS). 4. Add 1 to the RS. 5. Divide 100 by the number arrived at in step 4 above. 6. Subtract the number arrived at in step 5 above from 100. The resulting figure is the 14-day RSI expressed as a percentage.


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For energy futures the calculated 3-day and 14-day RSIs are interpreted as follows. ●

●

●

●

If the 14-day RSI is over 75%, the futures contract is overbought, and it may be difficult for prices to move any higher. Prices should soon correct to the downside more severely than if the 3-day RSI were overbought. If the 14-day RSI is below 35%, the futures contract is oversold, and prices should find support, i.e., the market should find it difficult to move lower. Prices should correct to the upside. If the 3-day RSI is over 90%, the market is very overbought, and if the market is trading technically, it will probably struggle to move higher. The market should have a good intraday correction to lower prices. If the 3-day RSI is below 20%, the market is very oversold, and if the market is trading technically, it will struggle to move lower. In this case the market should have a good intraday recovery to higher prices.

The guiding principle of the combined use of short and long RSIs is that if the market is looking overbought or oversold on the 3-day and 14-day RSIs, you can gauge that more than just a one-day price correction may be seen; a price correction could be seen over several days. Figures 11.14 and 11.15 demonstrate the moving average trendlines for NYMEX WTI and IPE Brent crude oil. Moving Averages There are three types of moving averages available: simple, weighted, and exponential. The critical element in a moving average is the number of time periods used in calculating the average. When using hindsight, it is always possible to find a moving average that would have been profitable. The 39-week moving average has an excellent track record in timing the major (long-term) market cycles. In energy markets on daily bar charts a 13-day moving average based on closing (or last) traded price gives a very good buy/sell signal. A 13-day simple moving average (based on last market close price) can also prove very profitable as a buy/sell indicator for the oil futures markets. It is a Fibonacci number, but the reality is probably that this

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11.14

NYMEX WTI crude oil


moving average has been highly publicized in the energy market, and plenty of traders watch it closely. As with all technical indicators, simple moving averages should never be used on their own as they do have some disadvantages. One such disadvantage is that you have to wait for the market close to get the final indication whether to buy or sell! However, it can be a very valuable confirmation tool to add to your other indicators and build up your view of the market. INTERPRETATION The most popular method of interpreting a moving average is to compare the relationship between a moving average of the commodities price with the commodities price itself. A buy signal is generated when the security’s price rises above its moving average, and a sell signal is generated when the security’s price falls below its moving average. The drawback to


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11.15

IPE Brent moving average


moving averages is that they can create false signals if the market is “range bound,” although they work really well in a trending market. Table 11.1 shows the 13-day moving average for NYMEX WTI (Figure 11.16). The length of a moving average should fit the market cycle you wish to follow. T A B L E

11.1

13-day moving average for NYMEX WTI Trend

Moving average

Very short term Short term Minor intermediate Intermediate Long term

5–13 days 14–25 days 26–49 days 50–100 days 100–200 days

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F I G U R E

11.16

Double top

Source: Tom James

F I G U R E

11.17

Double bottom

Source: Tom James

Double Tops and Bottoms This is an example of a trend reversal. The failure of prices to exceed the previous peak followed by a downside break of the previous low constitutes a downside trend reversal. Figure 11.17 shows a double top. This is an example of a bottom reversal pattern. Usually the first sign of a bottom is the ability of prices to hold a previous very recent low. This is then confirmed once recent resistance is broken. Volume should look to increase and the speed of the market moving away from the bottom should increase. CONCLUSIONS There are a large number of tools that can be used for technical analysis of the market, and it is important that they be used in combination with each other. But even if the five or six most appropriate analytical tools


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have been chosen to study the prevailing conditions, the results may still not always prove utterly reliable. The truth is that in the real world, there are some days when technical factors drive the market and others when it is driven by fundamentals. The key is to identify and keep asking the same questions over and over again: What is driving the market—technical factors or fundamentals? Is there fundamental news which has not yet been absorbed by the market price or, in turn, by the technical analysis? Successful traders know that important fresh news will always be in the price before it turns up in the charts. REFERENCE Wilder, J Welles (1978) New Concepts in Technical Trading Systems (Greensboro, NC: Trend Research).


C H A P T E R

12

Risk Management in Energy-Focused Commodity Futures Investing Hilary Till

INTRODUCTION This chapter will discuss the practical issues involved in applying a disciplined risk management methodology to energy-focused futures investing. The chapter will show how to apply methodologies derived from both conventional asset management and hedge fund management to futures investing. It will also discuss some of the risk management issues that are unique to leveraged futures investing. RISK IS THE FLIPSIDE OF RETURN In a number of derivatives trading strategies, an investor is paid to bear risks that others would prefer to lay off or not take on. What John Maynard Keynes (1935) wrote is just as true today: “The violence of the fluctuations which normally affect the prices of many individual commodities shows what a great risk the short-period speculator in commodities runs, for which he requires to be remunerated on a corresponding scale.” A number of derivatives trading strategies are well known and publicized, which does not prevent them from continuing to exist. For example, we have found that trades that have appeared in 1980s commodity brokerage recommendations and have been published in the Journal of Futures Markets and other empirically oriented journals are still valid today. 167

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In discussing consistently profitable grain futures trades, Cootner (1967) stated that the fact that they persist in the face of such knowledge indicates that the risks involved in taking advantage of them outweigh the gain involved. This is further evidence that . . . [commercial participants do] not act on the basis of expected values; that . . . [these participants are] willing to pay premiums to avoid risk.

In a number of statistically significant futures trades, the investor who implements these trades assumes some specific event risk that others do not want to assume, which is why we believe that there is a return to efficiently bearing this risk in the first place. THE MOST IMPORTANT ELEMENT OF AN INVESTMENT PROCESS The key to a successful investment program is not in discovering proprietary investment strategies: a diligent literature search will turn up a great number of strategies, as noted above. Instead, the most important element of an investment process may well be how one implements the program’s portfolio construction and risk management methodologies so that one can have somewhat smooth performance and stay in business during dramatic market moves. This point will be further elaborated on below. PRODUCT DESIGN ISSUES In derivatives trading, one has a lot of flexibility in designing an investment program. Futures trading requires a relatively small amount of margin. For example, in some futures programs one only needs to set aside about $7 for each $100 of exposure. The result is that one can easily adjust one’s leverage level to magnify gains (and, of course, magnify losses). Trade sizing is mainly a matter of how much risk one wants to assume. An investor is not very constrained by the amount of initial capital committed to trading. With the use of options, one can also be very particular about the risks that the investor wishes to hedge away by paying option premia. We believe that what leverage level is chosen for a program and which risks are hedged are product design issues. One needs to determine how the program will be marketed and what the client’s expectations will be. For example, a number of top commodity trading advisers have had losses in excess of 30%, which seem to have been acceptable to their

CHAPTER 12 Risk Management in Energy-Focused Commodity Futures Investing

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clients since these investment programs sometimes produce annual returns in excess of 100%. Investors know upfront the sort of swings in profits and losses to expect from such managers. Another example is if an alternative investment program were advertised as an equity diversifier, then clients would expect that the program should not do too poorly in the face of a large equity-market decline. The parameters of a program’s risk management policy should directly flow from the return, risk, and correlation expectations of the program’s client base. When attempting to adhere to these top-level parameters, the actual implementation of a program’s risk management policy will rely heavily on the particular assumptions about the statistical properties of futures prices, as will be discussed later. VIABILITY OF A FUTURES PROGRAM As already touched upon, our belief is that a number of statistically significant investment opportunities exist because of the possibility of rare, but nonetheless large, losses. One can build a business or investment program around these positive expected-value opportunities, but the particular leverage level and hedging strategy chosen will determine the ongoing viability of the program. The basic investment strategies employed by the following were and are backed by historical experience: ●

●

●

the savings and loan industry in the 1980s in exploiting a persistently steep yield curve; Metallgesellschaft in 1993 in exploiting the persistently backwardated shape of several energy futures contracts; Long Term Capital Management in 1998 in profiting from convergence trades in the fixed-income markets.

All the investment strategies noted above are statistically valid, yet resulted in billions of dollars of losses. Obviously, in retrospect, the leverage level and hedging strategies chosen by these institutions were flawed. STANDARD RISK MANAGEMENT METHODOLOGY The way that risk management has been applied by conventional asset managers is typically as follows.

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●

● ●

●

●

Translate the client’s guidelines into return and risk targets with respect to an index or benchmark. Determine the active bets away from a program’s benchmark. Make assumptions about the expected returns, volatility, and correlation of the active bets. Construct the client’s portfolio so that the client’s return and risk targets will be achieved if one’s statistical assumptions are correct. Continually monitor the portfolio’s actual return and risk performance for adherence to the established targets.

Litterman (1996) noted that “The art of successful portfolio management is not only to be able to identify opportunities, but also to balance them against the risks that they create in the context of the overall portfolio.” Risk management is therefore designed into the investment process. The conventional asset manager approach to risk management is a useful first step in designing a risk management program for leveraged futures trading. However, one still needs to add several layers of risk management to this approach because of the unique statistical properties of commodity futures contracts and because of the different way futures products are marketed. A futures product typically does not have a benchmark, so the conventional asset manager approach of translating a client’s guidelines into risk and return targets with respect to an index does not directly apply. Instead, one needs to determine the acceptable trade-off between total return and total risk for a client. Given the ability to leverage, a number of commodity trading advisers offer one-times, two-times, and three-times versions of the same program. A client can directly choose the leverage level for their investment based on their ability to tolerate losses of a given magnitude. The second step in a conventional asset manager approach to risk management consists of making assumptions about expected returns, risks, and correlations of active bets. It is at this point that the unique behavior of commodity prices creates extra steps in a risk management program. UNDERSTANDING PRICE BEHAVIOR Research from the 1970s showed that diversified portfolios of equities have returns that appear to be symmetrically distributed. It is a different matter for commodity prices.


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Deaton and Laroque (1992) note the following about the empirical behavior of the prices of a number of commodities. ● ● ●

●

●

●

“Commodity prices are extremely volatile.” There exist “rare but violent explosions in prices.” In normal times, there is a “high degree of price autocorrelation.” “In spite of volatility, prices tend to revert to their mean or to a . . . trend” level. “There is substantial positive skewness” in the price distributions. There is “substantial kurtosis with tails much thicker than those of the normal distribution.”

Commodity prices tend to exhibit positive skewness for the following reason. During times of ample supply, there are two variables that can adjust to equilibrate supply and demand: more inventories can be held and the price can decrease. But if there are inadequate inventories, only the price can respond to equilibrate supply and demand; given that, in the short run, new supplies of physical commodities cannot be instantly mined, grown, and/or drilled. VALUE AT RISK If a portfolio of instruments is normally distributed, one can come up with the 95% confidence interval for the portfolio’s change in monthly value by multiplying the portfolio’s recent monthly volatility by 2 (or 1.96, to be more exact). The portfolio’s volatility is calculated from the recent volatilities and correlations of the portfolio’s instruments. This is the standard valueat-risk approach. But this approach alone is obviously inadequate for a commodity portfolio, which consists of instruments that have a tendency toward extreme positive skewness. While this measure is useful, it has to be used jointly with other measures and actions. The measure is useful since one wants to ensure that under normal conditions, a commodity position has not been sized too large that one cannot sustain the random fluctuations in profits and losses that would be expected to occur, even without a dramatic event occurring. Sizing a trade based on its volatility is especially important the longer the frequency of predictability is. For example, if a trade’s predictability is at quarterly intervals, the trade has to be sized to withstand the daily

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fluctuations in profits and losses. In one extreme example, Lettau and Ludvigson (2001) have found that equities are predictable at business cycle frequencies. But that means that one cannot have a leveraged investment process to take advantage of this predictability. SCENARIO TESTING Using long-term data, an investor should directly examine the worst performance of a commodity strategy under similar circumstances in the past. In practice, we have found that such a measure will sometimes be larger than a value-at-risk measure based on recent volatility. We would recommend examining the worst performance of a futures trade over the entire time horizon of the trade rather than looking at what its worst performance was over a period of, say, three days. We believe that markets are “learning systems.” During a price shock, if a similar event occurred in the past, market participants know what the magnitude of the price move was during the past event. So an entire, dramatic price move may occur in a shortened time frame compared to the past. In practice, if a market only has limited historical data, it would be prudent to scale down the size of a position in such a market since one may not be able to get a complete idea of the range of possible outcomes. If one is relying on historical data to find pockets of predictability in the futures markets, then examining worst-case outcomes can also serve another purpose. If the loss on a particular commodity futures strategy exceeds the historical worst case, this can be an indication of a new regime that is not reflected in the data. This would trigger an exit from a systematic trade since one no longer has a handle on the worstcase scenario. A recent example of a fundamental structural change occurring in a commodity market was provided by Fusaro (2005). He reveals that in the summer of 2005, “the big Wall Street houses and some other hedge funds lost many . . . hundreds of millions [of dollars] on gasoline/heating oil spreads. They could not imagine that heating oil would go higher than gasoline in June. It just never happened before.” The conclusion from this discussion is that a commodity program will not experience the full brunt of a structural break if one exits a trading strategy after experiencing losses that are greater than have been the case in the past, as noted in Till (2006).


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DEEP OUT-OF-THE-MONEY OPTIONS In a systematic investment program based on historical data, one can make determinations about the expected return of an investment. One result is that an investor can decide to give up a small fraction of this expected return in order to hedge against catastrophic risk. An investor can do so with deep out-of-the-money options. This choice is especially advisable for commodity futures positions that require physical delivery at maturity. This means that contracts can be periodically squeezed to quite unpredictably high levels. EXIT STRATEGY Although, strictly speaking, not a risk management issue, we recommend an exit strategy for commodity investments that recognizes the meanreverting properties of commodities. In our case, this means examining historical data to determine the typical size of moves during supply– demand imbalances. DIVERSIFICATION AND CONCENTRATION RISK As discussed in Till (2001), a commodity investment manager can potentially set up dampened risk portfolios of commodity investments, which are very nearly uncorrelated with each other. For example, Figure 12.1 shows the annualized portfolio volatility versus the number of commodity strategies for a portfolio from June 2000. On the basis of three months of price data, these strategies had correlations among each other of between – 20% and + 20%. The figure demonstrates the beneficial effect on portfolio volatility of incrementally adding unrelated trades. Now for all types of leveraged investing, a key risk management concern is inadvertent concentration risk. So, for example, equity option market-makers will try to ensure that their book of trades does not have inadvertent style and industry concentrations by using tools like the risk management software package, Barra. In leveraged commodity futures investing, one must be careful with commodity correlation properties. Humphreys and Shinko (1997) discuss how correlations among commodity markets can be highly seasonal. Their specific example discusses the correlation of natural gas in different regions, which depends on whether it is summer or winter.

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F I G U R E

12.1

Annualized portfolio volatility

Source: Till (2000), Exhibit 5. Copyright © Institutional Investor, Inc

In our own work, we have found that seemingly unrelated commodity markets can become temporarily highly correlated. This becomes a problem if commodity managers are designing their portfolios so that only a certain amount of risk is allocated per strategy. The portfolio manager may be inadvertently doubling up on risk if two strategies are unexpectedly correlated. UNDERSTANDING THE FUNDAMENTAL DRIVERS OF A STRATEGY The antidote for this problem is twofold. One solution is to understand what the key factors are which drive a strategy’s performance, and a further solution is to use recent short-term data in calculating correlations. If two trades have common drivers, then it can be assumed that their respective performances will be similar. Recent data can frequently capture the time-varying nature of correlations that long-term data average out. Example: Corn and Natural Gas Figures 12.2 and 12.3 are a stark example from summer 1999 of how seemingly unrelated markets can become temporarily very related. Normally, natural gas and corn prices are unrelated; sampling every three days,


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12.2

September corn futures prices versus September natural gas futures prices (November 30, 1998 to June 28, 1999)


Figure 12.2 shows a correlation of +12%. But during July they can become highly correlated; Figure 12.3 shows, during a three-week period in July 1999, a correlation of + 85%. Depending on the values of key fundamental drivers, two prospective trades in the summer are to be short these two commodities. Now, the empirical evidence seems to show that these two trades may be the same trade. So, if one puts both these trades in their F I G U R E

12.3

September corn futures prices versus September natural gas futures prices (June 29, 1999 to July 21, 1999)


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portfolio, one would be inadvertently doubling up on risk. How could these two seemingly different trades be, in fact, the same trade? To answer this question, one needs to understand why these two trades tend to work. They are part of a class of trades called “weather fear premium” trades. In this class of trades, as explained by DiTomasso and Till (2000), A futures price will sometimes embed a fear premium due to upcoming, meaningful weather events. One cannot predict the weather, but one can predict how people will systematically respond to upcoming weather uncertainty. In this class of trades, a futures price is systematically too high, reflecting the uncertainty of an upcoming weather event. We say the price is too high when an analysis of historical data shows that one can make statistically significant profits from being short the commodity futures contract during the relevant time period. And further that the systematic profits from the strategy are sufficiently high that they compensate for the infrequent large losses that occur when the feared, extreme weather event does in fact occur.

In Till (2000), we gave several examples of this strategy. The key pollination period of corn is around mid-July: Its key pollination period is about the middle of July. If there is adverse weather during this time, new-crop corn yields will be adversely affected. This means that the new-crop supply would be substantially lessened, dramatically increasing prices. A systematic trade is to short corn futures from June through July. There is systematically too high a premium embedded in corn futures contracts during the pre-pollination time period.

Turning to natural gas, In July, there is fear of adverse hot weather in the US Northeast and Midwest. Air conditioning demand can skyrocket then. From June to midJuly, a systematic trade is to short natural gas futures contracts at the height of a potential weather scare.

Both the July corn and natural gas trades are heavily dependent on the outcome of weather in the US Midwest. In July 1999 the Midwest had blistering temperatures, which even led to some power outages. During that time, both corn and natural gas futures prices responded in nearly identical fashions to weather forecasts and realizations.


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12.4

Daily prices of corn and natural gas futures (June 10, 2005 to July 22, 2005)

Source: Till and Eagleeye (2005a), Figure 5

Figure 12.4 shows an updated example from summer 2005. Because both corn and natural gas have common reactions to the possibility of extreme heat, their prices sometimes wax and wane at similar times during the summer, as would be expected from the discussion above.

Example12.1: Crude Oil, Soybeans, and Copper One might expect that the price of crude oil should not be correlated with the prices of either soybeans or copper. But in reviewing Figures 12.5 and 12.6 from the first half of 2005, one might question that expectation. What might explain the common waxing and waning of prices in crude oil, soybeans, and copper during the first half of 2005? Howell (2005) points out that China, with its population of 1.3 billion, is now the world’s largest consumer of copper, steel, iron ore, and soybeans, and the second largest consumer of energy. When one re-examines Figures 12.5 and 12.6 in light of the Chinese holiday calendar, one notes that the lulls in each commodity’s bull market occurred around the time of Chinese holidays in February and May 2005, presumably when Chinese demand was absent.

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F I G U R E

12.5

Daily prices of crude oil futures versus (top) soybeans futures and (bottom) copper futures (January 5, 2005 to March 24, 2005)

Source: Till (2005)


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12.6

Daily prices of copper futures versus (top) crude oil futures and (bottom) soybean futures (April 1, 2005 to May 27, 2005)

Source: Till and Eagleeye (2005a), Figure 7

If a commodity portfolio manager does not want to own too much risk to fluctuating Chinese demand, then it would be prudent for the manager to be careful in his or her risk capital allocation to the petroleum complex, industrial metals, and soybeans. Our conclusion is that in order to avoid inadvertent correlations, it is not enough to measure historical correlations. Instead, an investor needs

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to have an economic understanding for why a trade should work in order to best be able to appreciate whether an additional trade will act as a portfolio diversifier. EXTRAORDINARY STRESS TESTING As discussed above, risk management policies flow from product design decisions. Futures products are typically marketed as equity investment diversifiers. Therefore one job of risk management is to attempt to ensure that a futures investment will not be correlated to the equity market during periods of dramatic equity losses. This is not an issue for, say, an equity mutual fund. During a time of stress in the equity markets, clients would expect that their equity fund would perform poorly. This extra risk management step is unique to alternative investments, again, because of the way they are marketed. For example, funds of hedge funds are also marketed as equity diversifiers, so this is also a particular area of concern for such funds. Since funds of funds typically include a lot of arbitrage strategies, which in turn rely on the ability to leverage, funds of funds are at risk to liquidity shocks. And the equity markets typically also do poorly during liquidity shocks. One solution advanced by a prominent fund-of-funds manager is to include an interest rate overlay in their fund. The interest rate overlay consists of going long Eurodollar (short-term US interest rate) futures, which do well when short-term interest rates are cut. The Federal Reserve Board’s response to liquidity shocks during the last 19 years has been to cut short-term interest rates so a Eurodollar overlay could plausibly offset losses in portfolios consisting of arbitrage strategies. This type of macro-hedging is very applicable to commodity and financial futures investments as well. A number of commodity futures strategies have a long commodity bias since they rely on taking an inventory risk that commercial participants wish to lay off. One consequence is that these strategies are at risk to sharp shocks to business confidence. And during sharp shocks to business confidence, as occurred in the aftermath of September 11, 2001, the stock market performs quite poorly. A number of financial futures strategies involve taking long positions in relatively illiquid markets and short positions in liquid markets during predictable times of increases in market liquidity. One consquence is that these strategies are at risk to liquidity shocks, as occurred during the fall of 1998 at the time of the Long-Term Capital Management/Russian default crisis.


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As noted before, the Federal Reserve Board, under former Chairman Alan Greenspan, has responded to financial shocks by cutting interest rates, which has resulted in the stock market stabilizing. As long as this type of policy continues, one way to hedge a portfolio that has exposure to shocks to business confidence or shocks to the availability of credit is to include a fixed-income hedge. The hedge could take the form of either a Eurodollar futures contract overlay or purchases of out-of-the-money fixed-income calls. This recommendation is similar to that of the fund-of-funds manager noted above, whose portfolios were at risk to liquidity shocks. Obviously, one would prefer to layer on natural hedges, which themselves have positive expected value. We have found that this is sometimes possible in a diversified futures program. For example, in the fall, there tend to be a number of statistically significant commodity trades that have a long bias. Also, at the same time, there are a number of statistically significant long fixed-income trades. By carefully combining these trades, the fixed-income trades operate as a natural hedge to the event risk taken on with the long commodity trades. The hedge fund world also provides other risk management solutions that are applicable to futures investments. One concern for a fundof-funds is that its group of funds is inadvertently exposed to some event risk, such as an emerging market shock. This issue is compounded by the fact that a hedge fund investor is frequently not allowed to see what a hedge fund is investing in because this is considered proprietary information by a hedge fund. One risk management software provider, Measurisk, had solved this problem by confidentially collecting hedge fund portfolios and directly determining their sensitivity to past financial shocks. For example, if one held a particular fund-of-funds portfolio during October 1987, one could see how that portfolio would have performed during the stock market crash. This scenario test gives an indication of sensitivity to a stock market crash. For a commodity and financial futures portfolio, we believe that it is prudent to examine how the portfolio would have performed during various well-defined stock market declines, given that such investments are marketed as equity portfolio diversifiers. Also, various crises have shown that the only thing that goes up during such times is correlation! If a portfolio shows sensitivity to certain extreme events when the stock market has declined, this does not necessarily mean that the portfolio should be sized differently or constructed differently. It may mean that a macro portfolio hedge would be advisable, such as purchasing out-of-the-money Eurodollar call options, as noted above.

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RISK MANAGEMENT REPORTS On a per-strategy basis, it is useful to examine each strategy’s: ● ● ● ● ●

value-at-risk based on recent volatilities and correlations; worst-case loss during normal times; worst-case loss during well-defined eventful periods; incremental contribution to portfolio value-at-risk; incremental contribution to worst-case portfolio event risk.

The last two measures give an indication if the strategy is a risk reducer or risk enhancer. On a portfolio-wide basis, it is useful to examine the portfolio’s: ● ● ●

value-at-risk based on recent volatilities and correlations; worst-case loss during normal times; worst-case loss during well-defined eventful periods.

Each measure should be compared to some limit, which has been determined based on the design of the futures product. So, for example, if clients expect the program to lose no more than 7% from peak to trough, then the three portfolio measures should be constrained not to exceed 7%. If the product should not perform too poorly during financial shocks, then the worst-case loss during well-defined eventful periods should be constrained to a relatively small number. If that worst-case loss exceeds the limit, then one can devise macro portfolio hedges accordingly. Now obviously, the danger with these recommended approaches is that one is relying on historical data for guidance since completely unprecedented events do happen. That is why we had earlier recommended exiting any futures trades in which the losses exceed those known in history since one is then in uncharted territory. The risk reports in Tables 12.1 and 12.2 give examples of a futures portfolio with the recommended measures displayed. Note, for example, the properties of the soybean crush spread. It is a portfolio event risk reducer, but it also adds to the volatility of the portfolio. Table 12.2 displays the recommended risk measures for another example portfolio. Note again the properties of the Eurodollar (short-term interest rate) futures. The interest rate position is a portfolio event risk reducer, as discussed previously, but it also adds to the volatility of the portfolio under normal conditions.


T A B L E

183

12.1

First example of a strategy- and portfolio-level risk report Worst-case Loss

Incremental Contribution

Strategy

Value-at- Normal Risk Times

Eventful Times

Portfolio Worst-Case ValuePortfolio at-Risk∗ Event Risk∗

Deferred reverse soybean crush spread Long deferred natural gas outright Short deferred wheat spread Long deferred gasoline outright Long deferred gasoline vs. heating oil spread Long deferred hog spread Portfolio

2.78%

–1.09%

–1.42%

0.08%

– 0.24%

0.66%

– 0.18%

– 0.39%

0.17%

0.19%

0.56%

– 0.80%

– 0.19%

0.04%

0.02%

2.16%

– 0.94%

– 0.95%

0.33%

0.81%

2.15%

–1.04%

– 2.22%

0.93%

2.04%

0.90% 3.01%

–1.21% – 2.05%

– 0.65% – 2.90%

0.07%

– 0.19%

∗A

positive contribution means that the strategy adds to risk, while a negative contribution means the strategy reduces risk.

Source: Risk Report from Premia Capital Management, LLC, as cited in Till (2002)

As Tables 12.1 and 12.2 show, an incremental-contribution-to-risk measure based solely on recent volatilities and correlations does not give sufficiently complete information about whether a trade is a risk reducer or risk enhancer. CONCLUSIONS Our view is that there are a number of derivatives strategies which earn returns due to assuming risk positions in a risk-adverse financial world. The returns are not necessarily due to inefficiencies in the marketplace. There is a very important active component to an investment program that earns a return due to bearing risk. It is the investment program’s risk management methodology and policy. An investment manager must decide how much to leverage the strategy and whether to give up any returns by hedging out some strategy’s extreme risks.

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T A B L E

12.2

Second example of a strategy- and portfolio-level risk report.∗ Worst-Case Loss

Incremental contribution

Strategy

Value-at- Normal Risk Times

Eventful Times

Portfolio Worst-Case value-at- Portfolio Risk† Event Risk†

Gasoline front-to-back spread Deferred outright gasoline Deferred outright natural gas Deferred Eurodollar futures Hog spread Deferred gasoline spread Cattle spread Portfolio

2.59%

– 5.59%

– 4.31%

1.62%

0.64%

3.81%

– 2.50%

– 2.76%

2.93%

– 0.72%

0.67%

– 0.15%

– 0.29%

0.52%

0.16%

2.42%

– 5.92%

– 0.96%

0.77%

– 2.86%

3.87% 1.60% 1.62% 9.24%

– 2.66% – 0.29% – 0.50% – 8.89%

– 3.23% – 0.53% – 1.34% – 2.27%

1.18% 1.33% 0.25%

– 0.29% 0.29% – 0.32%

∗While

under “normal” times, the gasoline spread position is less risky than the outright; during particular “eventful” times the spread adds to risk while the outright reduces risk. While under “normal” times, the Eurodollar futures position adds to risk; during particular “eventful” times this interest rate position reduces risk.

†A

positive contribution means that the strategy adds to risk, while a negative contribution means the strategy reduces risk.

Source: Risk Report from Premia Capital Management, LLC, as cited in Till and Eagleeye (2005b), Exhibit 18. Copyright © Institutional Investor, Inc

That investment manager must also continually monitor the risk exposures in his or her portfolio and make sure that those exposures adhere to pre-defined limits. In designing a risk management framework, a leveraged futures investor can use as a starting point the framework provided by conventional asset managers and also by fund-of-hedge-funds managers. We conclude by noting that how investors design and carry out their risk management policies is key to an investment program’s viability, especially in leveraged commodity futures investing. ACKNOWLEDGMENTS This chapter is largely based on two articles that were previously published by Commodities Now (http://www.commodities-now.com). The author


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wishes to express thanks to publisher Guy Isherwood for kind permission to reprint these two articles in this collection of risk management articles for the Professional Risk Managers’ International Association (http://www. prmia.org). The author would also like to note that the ideas in this chapter were jointly developed with Joseph Eagleeye, co-founder of Premia Capital Management, LLC (http://www.premiacap.com). REFERENCES Cootner, P (1967) Speculation and hedging, Food Research Institute Studies, Supplement, pp. 65–106. DiTomasso, J and Till, H (2000) Active commodity-based investing, Journal of Alternative Investments, Summer, pp. 70–80; available at http://www.premiacap.com/publications/ JAI_Sum00.pdf. Deaton, A and Laroque, G (1992) On the behavior of commodity prices, Review of Economic Studies, January, pp. 1–23. Fusaro, P (2005) Energy: an immature financial market, Energy Hedge, October 1, p. 1. Howell, R (2005) Investment Seminar, Schroders Alternative Investments Group, Commodities, Gstaad, February. Humphreys, B and Shimko, D (1997) Commodity risk management and the corporate treasury, in Financial Risk and the Corporate Treasury, p. 115 (London: Risk Publications). Keynes, J M (1935). A Treatise on Money: The Applied Theory of Money, Vol. II, pp. 136–139 (London: Macmillan). Lettau, M and Ludvigson, S (2001) Consumption, aggregate wealth, and expected stock returns, Journal of Finance, June, pp. 815–49. Litterman, R (1996) Hot Spots and Hedges, Goldman Sachs Risk Management Series, October, p. 50. Till, H (2000) Passive strategies in the commodity futures markets, Derivatives Quarterly, Fall, pp. 49–54. Till, H (2001) Taking full advantage of the statistical properties of commodity investments, Journal of Alternative Investments, Summer, pp. 63–66; available at http://www. premiacap.com/publications/JAI_Sum01.pdf. Till, H (2002) Risk management lessons in leveraged commodity futures trading, Commodities Now, September, pp. 84–87; available athttp://www.premiacap.com/ publications/CN_0902.pdf. Till, H (2005) Risk management in commodity futures trading. Presentation at GAIM Conference, Lausanne, June 6; available at http://www.premiacap.com/publications/ GAIM_060605.pdf. Till, H (2006) Portfolio risk measurement in commodity futures investments, in T. Ryan (ed.), Portfolio Analysis: Advanced Topics in Performance Measurement, Risk and Attribution (London: Risk Books). Till, H and Eagleeye, J (2005a) Challenges in commodities risk management, Commodities Now, September, pp. 45–50; available at http://www.premiacap.com/publications/ CN_0905.pdf.

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Till, H and Eagleeye, J (2005b) Commodities—active strategies for enhanced return, in R. Greer (ed.), The Handbook of Inflation Hedging Investments (New York: McGraw-Hill). See also Journal of Wealth Management, Fall, pp. 42–61; available at http://www. premiacap.com/publications/CN_Fall_05.pdf.

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The ISDA Master Agreement Ten Years On, ISDA 2002 Tom James

INTRODUCTION The International Swaps and Derivatives Association (ISDA) is the global trade association that represents leading participants in the privately negotiated derivatives industry. ISDA sets the standards for the financial services industry, and these standards are now widely used in the energy industry. In 1992, the “ISDA Master Agreement” (ISDA 1992) revolutionized the documentation and legal contract process surrounding swaps trading across all markets, including energy. The 1992 version, the first version, remains the most popular and is widely used in energy trading markets (especially oil price index swaps). THE ISDA AGREEMENT ISDA agreements are made up of two important parts: the ISDA Master Agreement is a standard format which does not change (an example is the amendment of the ISDA Master Agreement included at the end of this chapter as Appendix A for your reference) and the ISDA Schedule to the Master Agreement. The Schedule is the part that is negotiated between counterparties and contains information such as procedures on 187

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settlement, early termination, default, netting arrangements (if any), and banking details for both organizations party to the Master Agreement. Sometimes the Credit Support Annex is attached to this agreement as well, an example of which is also included as Appendix B at the end of this chapter. The majority of crude oil, petroleum products, and financial power and gas over-the-counter (OTC) derivatives (i.e. derivatives that are money settled and do not involve any physical delivery of the commodity—OTC swaps/options) use the 1992 ISDA Master Swaps Agreement Multi-currency Cross-border version. In addition to this, counterparties in the market generally use the Master Swaps Agreement with 1993 ISDA commodity derivatives definitions and the 2000 supplement to the 1993 ISDA commodity derivatives definitions. ISDA’s 2003 Operational Benchmarking Survey found that the use of master agreements has been steadily increasing. ISDA members reported that signed master agreements are in place with around 90% of their OTC derivatives counterparties, compared to around 92% in 2002 and 85% in 2001 (www.isda.org). Development in markets and derivative market disasters like the collapse of Enron prompted a major review of the ISDA Master Agreement. In January 2003, ISDA issued its first full revision of the 1992 Master Agreement (Multi-currency Cross-border version). The product of several months’ consultation and amendment, the 2002 Master Agreement (ISDA 2002) builds upon and amends many of the provisions of its predecessor. Practically speaking, not many companies have yet adopted the 2002 ISDA Master Agreement, but it is slowly being introduced by energy market participants, so it is important for us to understand the key differences between the 1992 and 2002 versions. For this reason in this chapter we will focus on: ●

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a more detailed breakdown of the still market-dominant ISDA 1992 agreement; the main differences between ISDA 2002 and ISDA 1992 Master Agreements; the procedure to follow in order to upgrade an existing 1992 ISDA Master Agreement that may be placed with another counterpart to a 2002 ISDA Master Agreement.

CHAPTER 13 The ISDA Master Agreement, ISDA 2002

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THE ISDA MASTER AGREEMENT Any non-legal manager taking his or her first look at an ISDA agreement usually has a shock. It is a voluminous document and consists of 14 sections, as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14.

Interpretation Obligations Representations Agreements Events of default and termination events1 Early termination Transfer Contractual currency2 Miscellaneous Offices Expenses Notices Governing law and jurisdiction3 Definitions.

USEFUL ISDA PUBLICATIONS ISDA publishes some other very useful books to help business managers understand the meanings of the contract sections of its agreements, in particular the meaning of the 1993 and 2000 supplementary ISDA agreements and terms. The following are representative of their reference guides. 1. 1993 ISDA Commodity Derivatives Definitions. These definitions are designed to facilitate the documentation of commodity transactions under the 1992 Master Agreements. Sample forms of confirmation are included. 2. 2000 Supplement to the 1993 ISDA Commodity Derivatives Definitions. The Supplement is an update of the 1993 ISDA Commodity Derivatives Definitions (the “1993 Definitions”), which many participants in the OTC commodity derivatives markets have incorporated into existing confirmations or other

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agreements. As is the case with the 1993 Definitions, the Supplement is designed for use by participants in the markets for commodity derivatives transactions in documenting cash-settled commodity swaps, options, caps, collars, floors, and swaptions or such other cash-settled commodity derivatives transactions as the parties desire. The Supplement includes additional commodity reference prices for energy, metals, and paper. The Supplement may not include all the commodity reference prices available for a particular commodity and used by market participants, but it adds significantly to the number of commodity reference prices set forth in the 1993 Definitions and includes the Commodity Reference Price Framework from the 1993 Definitions, which facilitates the definition of a commodity reference price that is not set forth in the Supplement. In addition to an expanded Commodity Reference Price Section, the Supplement allows parties to incorporate price materiality into the Price Source Disruption Event. 3. 2000 ISDA Definitions and Annex. This is what the majority of players in the energy market are using at the moment, although this may change in the not-too-distant future.

PRE-CONFIRMATIONS AND LONG-FORM CONFIRMATIONS Banks and financial institutions aim to have ISDA agreements negotiated and signed off within three months of beginning a negotiation, although it can often take between three and six months to have an agreement put in place with a counterparty. Because of the time it takes to set up an agreement, it is common to see counterparties trading with one another on the basis of an agreement being put in place eventually (or under negotiation while trading). Most risk management policies prohibit any trading before an agreement has been signed off by both counterparties; however, the commercial need to trade sometimes takes precedence over this policy (with management approval). But trading without an agreement does add considerable legal risk to a business, and if trading must go ahead with a counterparty, it may be better to use what is termed a “pre-confirmation” or a “long-form confirmation.”


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A pre-confirmation states the terms of the derivatives transaction and choices of provisions that would appear in the ISDA Master Agreement. The idea behind this is to commit counterparties to this wording before the agreement is signed. However, these are becoming less common due to a tightening of risk management policies over documentation and controls over trading prior to ISDA Master Agreements. These days, long-form confirmations are far less frequently used.4 Basically, this is a one-off derivatives contract for a specific deal which covers all the main eventualities. This type of confirmation is probably best for dealing with entities which are not regular trading partners and so do not warrant the legal cost of creating an ISDA agreement. It can also be helpful in situations in which there is an urgent need to trade, but an agreement has not been signed off yet. Long-form contracts should be used for short-dated plain vanilla derivatives, with a counterparty in a familiar jurisdiction. ISDA DOCUMENTATION PROCESSING When entering into an ISDA agreement, one of the counterparties will usually take the initiative and send its standard ISDA Schedule draft wording for the other party to review and comment on. As mentioned earlier, the ISDA Master Agreement is not changed by counterparties; the ISDA Schedule is the negotiated document. At this stage of proceedings, no negotiation has begun on the specific terms in the ISDA Schedule. Prior to negotiation on terms, the credit department must first process the counterparty details and pass the details of internally approved credit terms to the legal department which needs this for inclusion in the ISDA Schedule; this also affects whether or not Credit Support Annexes are required. Before rushing into the expense of processing legal documentation with a new OTC counterparty, it is useful to check the memorandum and articles of association of the counterparty’s organization. These are known as the “M and As” and provide the legal incorporation details of the organization, specifying what business functions it can carry out and sometimes what it is prohibited from doing. It is very important to check that there is nothing in the M and As of the firm that prevents it from entering into OTC derivative contracts with other companies. If the M and As are satisfactory, then both parties should be ready to put together an ISDA agreement.

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Although the ISDA Master Agreement is a standard document, there are areas of it which give rise to different types of risk for counterparties and are therefore often areas of negotiation in the Schedule. (Remember that the Schedule is where counterparties make the choices of how certain areas of the Master Agreement will affect their derivatives transactions.) These areas are as follows.5 ●

Legal risk – Section 1(b) Inconsistency. Where there is any inconsistency between the ISDA Master Agreement text and the ISDA Schedule, the latter will prevail. Another key point is that if there is any conflict between a confirmation and the ISDA Master and the Schedule, the confirmation will prevail for the trade the confirmation is recapping. This can contribute to operational risk, so trade confirmations must go out correctly. – Section 1(c) Single agreement. If trades are closed out, this section makes sure that the values of all trades between the two counterparties are calculated and netted off against each other, so only one payment is required between the two counterparties. This avoids a situation called “cherry picking,” where if a company has gone bankrupt, the liquidator can call in payments on trades that are profitable for the bankrupt client but refuse to pay out on trades which are not profitable. For example, imagine that counterparties A and B do two derivatives trades, with A making $2 million on one deal (it is a zero-sum game, so B is losing $2 million) and B making $1.5 million on the other deal (with A losing $1.5 million). In this situation, if B went bankrupt and Section 1(c) was not in place (because it had been deliberately excluded via the wording in the ISDA Schedule), then A could end up being forced to pay B $1.5 million (even though the net position is that B owes A $0.5 million). The single agreement concept reinforces the position that a liquidator cannot do this. It collapses and nets out the entire portfolio of derivatives trades into one single payment due to one counterparty or the other. – Section 5(a) Events of default are a key area. This covers a party’s failure to make any payment or delivery under Section 2 of the Master Agreement which covers the counterparty’s


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obligations. In the past, the energy industry adopted a grace period of three days on payments, but this is increasingly being shortened to sometimes a single day. This section also covers credit support default, misrepresentation, default under specified transactions (we look at this in more detail in the ISDA Schedule example later in this chapter), cross default, bankruptcy, merger, illegality, and credit event upon merger. – Section 7 Transfer of the agreement. Normally, counterparties are not allowed to transfer the ISDA agreement or any rights and obligations under it without written consent from the other party. There are a few exceptions to this rule, but these are rare instances where a counterparty wants to transfer the agreement to avoid an “event” (e.g. illegality, tax event, certain cases surrounding a merger) and a counterparty transfers the close-out money payable to it by a defaulting counterparty to another firm. – Section 8 Contractual currency. This protects counterparties from foreign exchange losses on settlement and close-out payments. – Section 9(d) Miscellaneous (remedies cumulative). When a counterparty is faced with another counterparty defaulting, it should not forget that the termination of derivatives trades is not the only course of action. A counterparty can leave the trades open or even sue for damages, if it chooses to do so. – Section 13 Governing law and jurisdiction. The majority of energy derivatives trades under ISDA outside the United States, even with American companies, are conducted under English law and the jurisdiction of the English courts. Under ISDA, there is a choice between English law and English courts or State of New York law and the jurisdiction of the courts of the State of New York and the US District Court located in the borough of Manhattan in New York. Counterparty risk – Section 5 Events of default and termination events. This is examined from a practical standpoint in the ISDA Schedule example later in this chapter.

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Market risk – Section 6 ISDA Master Agreement. This covers early termination and in particular automatic early termination. We will look at this in the ISDA Schedule example later in this chapter. Documentation risk – Section 4 Agreements. This covers the agreement of what documents both counterparties agree to provide one another (e.g. company certificates of incorporation, copies of licenses and renewals). It also covers an agreement that in some cases counterparties must maintain certain licenses and also pay any stamp duty taxes on any agreement, etc. Payment on settlement risk – Section 2. This key area is where counterparties agree on details of how payments are to be made, how netting is performed, and it also covers provisions protecting counterparties against withholding tax deductions.

TRADING BEFORE AN ISDA IS SIGNED There is a documentation risk in the time period between the execution of an OTC derivatives trade and an agreement being agreed upon and signed. If a trade does take place prior to an ISDA agreement being signed between the two counterparties (which is not advisable, unless there are considerable commercial pressures to put a hedge on very quickly), then the trade confirmation sent out will normally state that both counterparties to the deal must use “best endeavors” (a legal term as to the amount of effort used to achieve an agreement) to enter into an ISDA agreement. In the confirmation it usually states that the derivatives trade is subject to the terms of an ISDA Master Agreement without a Schedule, so it is basically unamended. The lack of a Schedule, though, means that the two counterparties cannot make their own choices over key issues in the Master Agreement. These issues would include choices over what triggers automatic early termination of derivatives deals, payment netting and methods, what happens if a company merges with another, termination currency, tax representations (regarding withholding taxes on settlement payments), credit support (any parent companies willing to support the credit exposure on the


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derivatives trades), and which entities are included in “specified entities” (the other companies that are included in the agreement for the purposes of triggering a default). The biggest risk for an organization if it trades without an ISDA agreement is that if the other counterparty goes into bankruptcy or liquidation, a liquidator could end up “cherry picking” any profitable deals. THE MAIN DIFFERENCES BETWEEN ISDA 2002 AND THE ISDA 1992 MASTER AGREEMENT Although ISDA 2002 and ISDA 1992 are similar agreements in many ways, substantial revisions have been made to some of the more fundamental provisions of ISDA 1992. Some of the key changes are as follows: Executive Summary ●

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The reduction in grace period for failure to pay and other events of default, including bankruptcy. A new event of default for repudiation of the agreement. A redrafting of the cross-default event of default. The amendment of the illegality termination event. The introduction of a force majeure termination event. An amended hierarchy of events where circumstances arise which are capable of giving rise to an illegality and/or force majeure, or some other termination event or an event of default. The replacement of first method and second method, market quotation and loss, with a single valuation method for payments on early termination—the close-out amount. The incorporation of a set-off provision and the consolidation of the various interest provisions.

In terms of operations relating to ISDA-based swaps deals, the most interesting development is confirmations. Some people think this is just a fairly technical difference between ISDA 2002 and ISDA 1992 regarding trade confirmations. Under ISDA 2002, confirmation can finally be executed and delivered by counterparties by an exchange of e-mails. It should be noted that only the Agreement can be executed and delivered by fax or electronic messaging system (which ISDA 2002 differentiates from e-mails).

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Notices or other communications in respect of events of default, termination events, and early termination may not be given by either e-mail or electronic messaging system. Other Key Changes ISDA 2002 contains similar events of default to ISDA 1992, although there have been some changes to the description of some of these events of default. ●

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Failure to pay or deliver. A failure to pay or deliver must be remedied within one local business day (or one local delivery day in the case of deliveries) of notice of such failure being given to the relevant party in order to avoid an event of default. ISDA 1992 allowed a three local business day grace period. Breach of agreement; repudiation of agreement. ISDA 2002 incorporates a new subsection giving rise to an event of default if a party disaffirms, disclaims, repudiates, or rejects, in whole or in part, or challenges the validity of the Master Agreement, any confirmation or any transaction evidenced thereby. This subsection is similar to, and is in addition to, the credit support default under Section 5(a)(iii)(3) of ISDA 1992 in respect of credit support documents. Credit support default. The failing or ceasing of any security interest granted by a party or a credit support provider to the other party pursuant to a credit support document can give rise to an event of default. Default under specified transaction. This section has been amended to separate (1) defaults in making payment on the last payment or exchange date (or any payment on an early termination), (2) defaults in making any delivery, (3) any other defaults (other than delivery), and (4) disaffirming, disclaiming, repudiating, rejecting, or challenging the validity of a specified transaction. Delivery default and other defaults require the subsequent liquidation or acceleration of obligations under the relevant specified transaction (in respect of all defaults excepting delivery) or all transactions outstanding under documentation applicable to that specified transaction (in respect of delivery default only). Final payment default allows a grace period of


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one day but requires no further knock-on effects in order to constitute an event of default. Each of the defaults except for final payment default now refers expressly to a default under any credit support arrangement relating to a specified transaction as being capable of giving rise to an event of default under this heading. The definition of “specified transaction” under ISDA 2002 expressly excludes transactions under the agreement. Cross-default. The first paragraph in the event of default has been amended to clarify that the threshold amount relates to the size of the aggregate principal amount of the agreements or instruments in respect of which there has been a default. Whereas this is probably what was intended by ISDA 1992 as well, there was perhaps room for debate as to whether the threshold amount applied to the size of the specified indebtedness or the size of amounts involved in the default. Bankruptcy. Although the provisions are largely the same in ISDA 2002 as they were in ISDA 1992, there have been changes made to the applicable grace periods. Where a party institutes, or has instituted against it by a regulator, supervisor, or any similar insolvency officer, insolvency or bankruptcy proceedings, it would appear that an event of default will arise immediately, without reference to any grace period or the entering of any judgment. Where proceedings are instituted against it by any other entity, such proceedings can give rise to an event of default if either (1) they are not dismissed within 15 days or (2) judgment is entered. The grace period under ISDA 1992 was 30 days. Also a reduction in grace period has been made with respect to circumstances where a secured party takes steps to enforce its credit security. Termination events. The principal differences between the termination events in ISDA 1992 and ISDA 2002 are the expanded section concerning illegality and the inclusion of force majeure. Unlike ISDA 1992, ISDA 2002 does not make express reference to a change in law or interpretation and merely requires that the illegality be due to an event or circumstance (other than any action taken by a party or, if applicable, a credit support provider of such party) occurring after a transaction has been entered into. The subsection dealing with illegality

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of a transaction has been changed to make it clear that the illegality should affect the office through which payments and deliveries are effected in respect of a transaction and that the ability to take receipt of payments and deliveries is also included. The subsection dealing with illegality in respect of a credit support document has been restricted to cover only obligations to make or receive payments or deliveries or compliance with any other material provision of the affected credit support document. Force majeure. ISDA 2002 includes a provision dealing with force majeure. It is basically like the optional “impossibility” provision, which was suggested within the user’s guide to ISDA 1992. Force majeure, like illegality, has been made officespecific and expressly includes the ability to take receipt of deliveries and payments as well as the ability to make them. Of potential concern to counterparties to the ISDA 2002 is the expansion of force majeure to include circumstances not only where performance is pretty much impossible, but also where the affected trading office is prevented from performance or where performance is impracticable. Like illegality, force majeure can arise in respect of a transaction or in respect of a credit support document; the provision is split into two subsections, each covering one of the two options. The main difference between the operation of force majeure and illegality is that force majeure requires not only that the relevant cause be outside the control of the affected office, party, or credit support provider but will only apply if such office, party, or credit support provider could not overcome the prevention, impossibility, or impracticability having used reasonable efforts such as would not require such party etc. to incur more than incidental losses. You have to work hard to make sure that you really cannot get around the problems faced by the trading office that is affected. Credit event upon merger. This termination event has been amended in two ways: first, by a redrafting of the section by reference to separate designated events (the first of which being the equivalent ISDA 1992 termination event), the inclusion of an express requirement to take account of any credit support document and by the expanding of the equivalent ISDA 1992


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wording to include the transfer of a substantial part of a party’s assets as well as reorganization, reincorporation, and reconstitution; second, by the addition of two, new, designated events. The first of these is the acquisition of an ownership interest in a party by any person related to an entity, enabling such person to control that party. The second is the making by a party of any substantial change in its capital structure by means of the issuance, incurrence, or guarantee of debt or the issue of either (1) preferred stock or other securities convertible into debt or preferred stock or (2) an ownership interest in that party. These new designated events were not previously included in the ISDA 1992 and do seem open to a fairly broad interpretation. Deferral of payments. ISDA 2002 introduces deferral provisions which will be effective upon the occurrence of an illegality or force majeure. The new provisions defer any payment or delivery obligations under a transaction affected by illegality or force majeure so that such obligation does not become due until the earlier of (1) the first local business day (or local delivery day in the case of deliveries) after the applicable waiting period and (2) the date on which the event or circumstance giving rise to the illegality or force majeure ceases to exist. The waiting periods are set out in ISDA 2002 as three local business days in respect of illegality and eight local business days in respect of force majeure. However, this will be reduced to zero in each case in respect of illegality or force majeure affecting credit support documents where delivery or payment is actually required on the relevant day. Close out netting—early termination. Although the main difference between the early termination provisions of ISDA 1992 and ISDA 2002 is the differing method of valuation, there are a number of other changes, many of which build upon the newly expanded illegality and the newly introduced force majeure termination events. Rights to terminate contracts. The provisions relating to the right to terminate following an event of default in ISDA 2002 are unchanged from ISDA 1992. Also the provisions relating to the right to terminate following a termination event (save for the exclusion of illegality from the list of termination events giving rise to an obligation to transfer or reach agreement) remain

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unchanged. ISDA 2002 contains two new provisions that relate solely to illegality and force majeure. Unlike in respect of the other termination events, except in certain limited circumstances, either party may designate an early termination date in respect of all or less than all affected transactions. If one party serves notice terminating less than all affected transactions, the other party may respond designating the same early termination date in respect of all affected transactions. In the case of illegality and force majeure affecting credit support documents, only the non-affected party can serve an initial notice terminating either all or less than all affected transactions. However, if less than all affected transactions have been terminated, the affected party does have the right to respond with a designation of an early termination date in respect of all affected transactions. Payments on early termination. Unlike ISDA 1992, which allowed parties to elect either first method or second method, market quotation, or loss and set out different methods of calculating the early termination amount owing depending on that election, ISDA 2002 only permits parties to use the closeout amount valuation method. The mechanics of arriving at an early termination amount owing once the close-out amount is established are similar in operation to calculating an amount owing on an early termination date once a settlement amount has been determined in accordance with the second method and market quotation election under ISDA 1992. The early termination amount will generally be equal to the sum of the close-out amount determined by the determining party (or half the difference between the close-out amounts determined by each party in the case of a termination following a termination event with two affected parties) and any unpaid amounts owing between the parties. Set-off. This provision is included for the first time in ISDA 2002. However, it is substantially similar to the suggested “set-off” provisions in the user’s guide to ISDA 1992. The effect of the provision is to enable the non-defaulting party or non-affected party (provided that all outstanding transactions are affected transactions) in circumstances where there is one such party to elect that any early termination amount owing be


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reduced to the extent of any other amounts owing between the parties. In order to satisfy the requirement for mutuality between the parties so that set-off applies, ISDA 2002 also incorporates a representation that parties are dealing as principals in respect of all transactions. Office multibranches. The provisions dealing with multibranch arrangements have been expanded in ISDA 2002. Counterparties are expressly prevented from having recourse to the head office of a multibranch party in respect of deliveries or payments deferred in accordance with the provisions of ISDA 2002 following an illegality or force majeure for as long as those deliveries or payments are so deferred. New deeming provisions have also been included whereby a party will be deemed to have entered into a transaction through its head office, unless otherwise specified in the applicable confirmation or agreement between the parties.

A P P E N D I X

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ISDA Agreement MARCH 2003 ISDA® International Swaps and Derivatives Association, Inc. AMENDMENT1 dated as of .................................. to the ISDA MASTER AGREEMENT dated as of .................................. between .................................................................... and .................................................................... (the “Agreement”)

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The parties have previously entered into the Agreement and have now agreed to amend the Agreement by the terms of this Amendment (this “Amendment”). The International Swaps and Derivatives Association, Inc. (“ISDA”) has published the 2002 Master Agreement. The parties wish to modify the Agreement to reflect certain provisions of the 2002 Master Agreement. The specific modifications that the parties wish to incorporate in the Agreement are set forth in the Attachment to this Amendment (the “Attachment”). The purpose of this Amendment is to amend the Agreement on the terms set forth in the Attachment. Accordingly, in consideration of the mutual agreements contained in this Amendment, the parties agree as follows: 1. Amendment of the Agreement The Agreement is amended in accordance with the amendments set forth in the Attachment. 2. Representations Each party represents to the other party in respect of the Agreement, as amended pursuant to this Amendment, that all representations made by it pursuant to the Agreement are true and accurate as of the date of this Amendment. 3. Miscellaneous (a) Entire Agreement; Restatement. (i) This Amendment constitutes the entire agreement and understanding of the parties with respect to its subject matter and supersedes all oral communication and prior writings (except as otherwise provided herein) with respect thereto. (ii) Except for any amendment to the Agreement made pursuant to this Amendment, all terms and conditions of the Agreement will continue in full force and effect in accordance with its provisions on the date of this Amendment. References to the Agreement will be to the Agreement, as amended by this Amendment. (b) Amendments. No amendment, modification or waiver in respect of the matters contemplated by this Amendment will be effective unless made in accordance with the terms of the Agreement. (c) Counterparts. This Amendment may be executed and delivered in counterparts (including by facsimile transmission), each of which will be deemed an original. (d) Headings. The headings used in this Amendment are for convenience of reference only and are not to affect the construction of or to be taken into consideration in interpreting this Amendment. (e) Governing Law. This Amendment will be governed by and construed in accordance with [English law][the laws of the State of New York (without reference to choice of law doctrine)]. IN WITNESS WHEREOF the parties have executed this Amendment on the respective dates specified below with effect from the date specified first on the first page of this Amendment.

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ATTACHMENT Amendments to Master Agreement 1. The terms of Section 6(d)(i) of the Agreement are amended in their entirety as follows: “(d) Calculations; Payment Date. (i) Statement. On or as soon as reasonably practicable following the occurrence of an Early Termination Date, each party will make the calculations on its part, if any, contemplated by Section 6(e) and will provide to the other party a statement (l) showing, in reasonable detail, such calculations (including any quotations, market data or information from internal sources used in making such calculations), (2) specifying (except where there are two Affected Parties) any Early Termination Amount payable and (3) giving details of the relevant account to which any amount payable to it is to be paid. In the absence of written confirmation from the source of a quotation or market data obtained in determining a Close-out Amount, the records of the party obtaining such quotation or market data will be conclusive evidence of the existence and accuracy of such quotation or market data.” 2. The terms of Section 6(e) of the Agreement are amended in their entirety as follows: “(e) Payments on Early Termination. If an Early Termination Date occurs, the amount, if any, payable in respect of that Early Termination Date (the Early Termination Amount) will be determined pursuant to this Section 6(e) and will be subject to any Set-off. (i) Events of Default. If the Early Termination Date results from an Event of Default, the Early Termination Amount will be an amount equal to (1) the sum of (A) the Termination Currency Equivalent of the Close-out Amount or Close-out Amounts (whether positive or negative) determined by the Nondefaulting Party for each Terminated Transaction or group of Terminated Transactions, as the case may be, and (B) the Termination Currency Equivalent of the Unpaid Amounts owing to the Non-defaulting Party less (2) the Termination Currency Equivalent of the Unpaid Amounts owing to the Defaulting Party. If the Early Termination Amount is a positive number, the Defaulting Party will pay it to the Non-defaulting Party; if it is a negative

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number, the Non-defaulting Party will pay the absolute value of the Early Termination Amount to the Defaulting Party. (ii) Termination Events. If the Early Termination Date results from a Termination Event: (1) One Affected Party. If there is one Affected Party, the Early Termination Amount will be determined in accordance with Section 6(e)(i), except that references to the Defaulting Party and to the Non-defaulting Party will be deemed to be references to the Affected Party and to the Nonaffected Party, respectively. (2) Two Affected Parties. If there are two Affected Parties, each party will determine an amount equal to the Termination Currency Equivalent of the sum of the Close-out Amount or Close-out Amounts (whether positive or negative) for each Terminated Transaction or group of Terminated Transactions, as the case may be, and the Early Termination Amount will be an amount equal to (A) the sum of (I) onehalf of the difference between the higher amount so determined (by party X and the lower amount so determined (by party Y) and (II) the Termination Currency Equivalent of the Unpaid Amounts owing to X less (B) the Termination Currency Equivalent of the Unpaid Amounts owing to Y. If the Early Termination Amount is a positive number, Y will pay it to X; if it is a negative number, X will pay the absolute value of the Early Termination Amount to Y. (iii) Adjustment for Bankruptcy. In circumstances where an Early Termination Date occurs because “Automatic Early Termination” applies in respect of a party, the Early Termination Amount will be subject to such adjustments as are appropriate and permitted by applicable law to reflect any payments or deliveries made by one party to the other under this Agreement (and retained by such other party) during the period from the relevant Early Termination Date to the date for payment determined under Section 6(d)(ii). (iv) Pre-Estimate. The parties agree that an amount recoverable under this Section 6(e) is a reasonable pre-estimate of loss and not a penalty. Such amount is payable for the loss of bargain and the loss of protection against future risks and except as otherwise provided in this Agreement neither party will be entitled to recover any additional damages as a consequence of the termination of the Terminated Transactions.” 3. The term “Termination Currency Equivalent” in Section 14 of the Agreement is hereby amended by replacing “Market Quotation or Loss (as the case may be)” with “Close-out Amount”. 4. The following terms are added to Section 14 of the Agreement in the appropriate alphabetical position: Close-out Amount” means, with respect to each Terminated Transaction or each group of Terminated Transactions and a Determining Party, the amount of the losses or costs of the Determining Party that are or would be incurred under then prevailing circumstances (expressed as a positive number) or gains of the Determining Party that are or would be realized under then prevailing circumstances (expressed as a negative number) in replacing, or in providing for the Determining Party the economic equivalent of, (a) the material terms of that Terminated Transaction or group of Terminated Transactions, including the payments and deliveries by the parties under Section 2(a)(i) in respect of that Terminated Transaction or group of Terminated Transactions that would, but for the occurrence of the relevant Early


Termination Date, have been required after that date (assuming satisfaction of the conditions precedent in Section 2(a)(iii)) and (b) the option rights of the parties in respect of that Terminated Transaction or group of Terminated Transactions. Any Close-out Amount will be determined by the Determining Party (or its agent), which will act in good faith and use commercially reasonable procedures in order to produce a commercially reasonable result. The Determining Party may determine a Close-out Amount for any group of Terminated Transactions or any individual Terminated Transaction but, in the aggregate, for not less than all Terminated Transactions. Each Close-out Amount will be determined as of the Early Termination Date or, if that would not be commercially reasonable, as of the date or dates following the Early Termination Date as would be commercially reasonable. Unpaid Amounts in respect of a Terminated Transaction or group of Terminated Transactions and legal fees and out-of-pocket expenses referred to in Section 11 are to be excluded in all determinations of Close-out Amounts. In determining a Close-out Amount, the Determining Party may consider any relevant information, including, without limitation, one or more of the following types of information (i) quotations (either firm or indicative) for replacement transactions supplied by one or more third parties that may take into account the creditworthiness of the Determining Party at the time the quotation is provided and the terms of any relevant documentation, including credit support documentation, between the Determining Party and the third party providing the quotation; (ii) information consisting of relevant market data in the relevant market supplied by one or more third parties including, without limitation, relevant rates, prices, yields, yield curves, volatilities, spreads, correlations or other relevant market data in the relevant market; or (iii) information of the types described in clause (i) or (ii) above from internal sources (including any of the Determining Party’s Affiliates) if that information is of the same type used by the Determining Party in the regular course of its business for the valuation of similar transactions. The Determining Party will consider, taking into account the standards and procedures described in this definition, quotations pursuant to clause (i) above or relevant market data pursuant to clause (ii) above unless the Determining Party reasonably believes in good faith that such quotations or relevant market data are not readily available or would produce a result that would not satisfy those standards. When considering information described in clause (i), (ii) or (iii) above, the Determining Party may include costs of funding, to the extent costs of funding are not and would not be a component of the other information being utilized. Third parties supplying quotations pursuant to clause (i) above or market data pursuant to clause (ii) above may include, without limitation, dealers in the relevant markets, end-users of the relevant product, information vendors, brokers and other sources of market information. Without duplication of amounts calculated based on information described in clause (i), (ii) or (iii) above, or other relevant information, and when it is commercially reasonable to do so, the Determining Party may in addition consider in calculating a Close-out Amount any loss or cost incurred in connection with its terminating, liquidating or re-establishing any hedge related to a Terminated Transaction or group of Terminated Transactions (or any gain resulting from any of them).

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Commercially reasonable procedures used in determining a Close-out Amount may include the following: (1) application to relevant market data from third parties pursuant to clause (ii) above or information from internal sources pursuant to clause (iii) above of pricing or other valuation models that are, at the time of the determination of the Close-out Amount, used by the Determining Party in the regular course of its business in pricing or valuing transactions between the Determining Party and unrelated third parties that are similar to the Terminated Transaction or group of Terminated Transactions; and (2) application of different valuation methods to Terminated Transactions or groups of Terminated Transactions depending on the type, complexity, size or number of the Terminated Transactions or group of Terminated Transactions.” “Determining Party” means the party determining a Close-out Amount.” “Early Termination Amount” has the meaning specified in Section 6(e).” “Non-affected Party” means, so long as there is only one Affected Party, the other party.” 5. The following terms in Section 14 of the Agreement are deleted in their entirety: “Loss”, “Market Quotation”, “Reference Market-makers” and “Settlement Amount”. 6. Part 1(f) of the Schedule is deleted in its entirety and the subsequent paragraphs are renumbered sequentially. In case the parties have used another designation for the paragraph of the Schedule specifying the selection of Market Quotation or Loss and First Method or Second Method, the reference herein to Part 1(f) of the Schedule shall be deemed a reference to that paragraph.

A P P E N D I X

B

Sample Letter

[ LETTERHEAD OF SIGNATORY ]

[Insert counterparty contact details]

Re: Amendment Agreement to the 1992 ISDA Master Agreement

Dear [insert name]: I enclose a form of bilateral Amendment Agreement recently published by the International Swaps and Derivatives Association. The purpose of the Amendment Agreement is to replace Market Quotation and Loss, the measure of damages provisions from the 1992 ISDA Master Agreement, with a new single measure of damages provision, Close-out Amount. By executing the Amendment Agreement,


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parties will obtain the benefit of the new single measure of damages contained in the 2002 ISDA Master Agreement, without having to replace their existing 1992 Master Agreement. The Amendment replaces “Market Quotation” and “Loss” with Close-out Amount and makes other corollary changes such as updating Section 6(e) Payments on Early Termination and dropping the First Method approach. Please review the Amendment Agreement and have it executed by an authorized signatory and returned to me. Please contact me if you have any further questions. Yours sincerely,

[Signatory’s Name] Enc.

NOTES 1. An event is anything that has to happen to trigger some action in the contract. For example, a counterparty going bankrupt is considered to be an event, which may then allow certain action to be taken under the ISDA agreement. 2. In the energy industry, this is normally the US dollar. 3. Normally US or English law, depending on the jurisdiction. 4. They get their name from the fact that they usually consist of about three meters of telex roll or fax paper! 5. The following notes refer to the ISDA 1992 Master Agreement, which is still the key agreement offered by energy counterparties at the time of writing.


C H A P T E R

14

Creation and Transfer of Price Risk in European Energy Markets Alessandro Mauro

INTRODUCTION During the past years, the entire world economy has been affected by a massive increase in energy prices. Yet only a few years ago, there was complete confidence that cheap energy would last indefinitely. This firm belief, together with natural resource endowments and political inertia, explains why many developed economies have reduced their growth forecasts, all other factors being equal. It has to be stressed that the current impact of high energy prices on world economies is due to the risky environment that has been in place for several decades. In this chapter, we will try to explain the nature of this environment by assessing a broad identification of risk in an entire economic system. The aim is to perform the kind of extended risk mapping commonly done for enterprises in order to better understand why, where, and how risk is generated, how it is transferred among agents and transformed, and who ultimately bears such risk. The focus will be on European price risk, and it will consequently be important to investigate the ways energy prices are formed. The figures and analysis presented will be mainly based on the current 25 member states of the European Union (EU-25), although most of the conclusions are valid for other energy-dependent countries and regions around the world. 209

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CREATION OF RISK It is possible to give a general description of the ways in which risk is created and transferred in an economic system. An energy sector as a whole can be depicted as a dynamic system which is interconnected with other outside energy sectors via many energy inputs and outputs (imports and exports). Most energy sectors will also possess internal extraction and production of energy sources. The total amount of energy inside the system, consisting of production plus imports minus exports, undergoes transportation and transformation processes before being finally delivered and consumed. Figure 14.1 gives a general graphical description of this process. In such systems, there are essentially two ways in which risk can arise. At the outset, there is always a primitive creation of risk. This primitive creation can either happen inside the system or be imported from an outside system. At the same time, the risk present in a system can exit the system through exports to other outside systems. On the other hand, any risk transferred among agents inside the system will not increase or decrease the total amount of risk in that system. It must be made clear that external and internal energy inputs do not automatically generate price risk. In fact, this will happen if and only if energy prices are variable and free to fluctuate. In this regard, not only is the market structure important, but also time scale has to be taken into account.1 There are energy sources whose prices continuously fluctuate, others which change every day, and still others which are fixed for days or months but can then move to new levels.2 The latter sources will show monthly or yearly price volatility, rather than daily. From the perspective

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14.1

Energy markets as a dynamic system

Source: Alessandro Mauro

CHAPTER 14 Creation and Transfer of Price Risk in European Energy Markets

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14.2

Evolution of energy prices in Europe, 1987–2004

Source: BP (2005)

of this chapter, which will not confine itself to daily volatility, every one of these sources will introduce risk into the system. A source whose price remains fixed for years will be considered not risky, and in any case it is really hard to find one with this feature. In order to better explain this aspect, it is worth considering Figure 14.2, which shows the evolution of energy prices in Europe over the last two decades.3 We should conclude that principal energy sources show fluctuating prices over a yearly time horizon. Bearing in mind the general framework previously outlined, it is important to discuss in detail the ways in which risk enters the European energy markets. We will frequently refer to Figure 14.3, which depicts in a simplified way the structure of the European energy system. The figure highlights the areas where risk can potentially arise and where it is potentially transferred along the supply chain.4 The left-hand part is often identified as the “upstream” sector, the middle part is the “midstream,” and at the right-hand end, there is the “downstream.” The main reason for risk arising in European energy markets is linked to the utilization of primary fossil energy sources (namely oil, natural gas, coal) and uranium. It is evident from Figure 14.3 that Europe is largely a net importer of each of these sources as indigenous production

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14.3

A simplified representation of the structure of the European energy system

Source: Alessandro Mauro on data from BP (2005)

only partially meets European energy demand. In fact, the EU possess just about 0.6% of the world’s proven oil reserves, about 2% of natural gas reserves, and about 19.5% of proven coal reserves. EU energy dependency was 48.1% in 2002, and since 1992, it has never fallen below 43%, meaning that structurally, nearly half of its annual energy consumption has to be imported (Eurostat, 2005). Imported primary energy sources share some common factors. They have to be transported, and their price is generally variable (see Figure 14.2) and expressed in US dollars. The latter element implies a double risk exposure—commodity prices and exchange rates—as European agents have to buy US dollars in order to pay for energy goods. Most of these energy sources are not exported and retraded outside Europe but are transformed and used in Europe. Consequently, the risk, having entered the European system, will be transformed and transferred into related risks in secondary energy sources but will not exit to other areas of the world.5 Primary energy sources extracted and produced in Europe, accounting for about half the consumption, bring considerable additional risk as they


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also necessarily present fluctuating prices. The general reason is a phenomenon called intra-fuel competition. Crude oil extracted in the North Sea will compete with oil coming from the Middle East (oil-to-oil competition). Natural gas from the Netherlands will be sold in the same markets as Russian or Algerian gas (gas-to-gas competition). It is consequently rational, in the highly import-dependent European energy sector, that import prices and internal ones are significantly correlated.6 Considering that external source prices are set internationally, it is easy to conclude that internal source prices are mostly set internationally, though not in deterministic ways. One potential difference between imported and internal energy sources is that imports are paid for in US dollars, certainly giving place to foreign exchange rate exposure and adding further large quantities of risk. In any case, due to intra-fuel competition, foreign exchange rate fluctuations will very often also affect internal energy source prices. Having clarified how price risk continuously enters the European energy market, it would be interesting to quantify such risk. This is no simple task as supply and demand elasticities have to be taken into account,7 but a rough idea can be given. If we consider only oil, natural gas, and coal imported in 2004 (see Figure 14.3), the increase in their yearly average prices from 2002 to 2004, shown graphically in Figure 14.2, amounts to an increase of about $63 billion in total import costs. This figure takes only imported energy prices into account and neglects foreign exchange rate exposure (which in this case should reduce that increase as Euro appreciated against US dollar in the same period) as well as freight rates, which are explicit if energy sources are bought on an FOB basis and implicit when they are bought on a CIF basis.8 RISK TRANSFER The main outcome of the analysis performed in the previous section is that a huge amount of price risk is continuously entering the European energy markets. This risk, in most cases, will be entirely borne by European energy importers. In fact, once risk enters a system, it is common for it to be transferred among operators. If the risk is not shifted on to operators outside the system (i.e. exported physically or financially), the total amount of risk present in the system will remain constant. The mechanisms and volume of this risk transfer depend on many factors, principally market structure, the risk aversion of the actors involved, and policy and

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regulation. In this section, we will clarify how risk is transferred within European energy markets. Crude oil is never consumed “as is” in final uses but undergoes refining processes, producing many oil products which are utilized by both intermediate industries and end users. Among these products, gas oil and fuel oil are two of the most relevant for downstream energy markets. Gas oil is used in transportation and for space heating purposes, while fuel oil is a fuel input in thermoelectric power generation. The European market in refined products is highly competitive, and the industry is quite large and developed, as Europe accounts for more than 17% of total world refining capacity. European refiners transform and move a significant amount of risk in the market since they not only buy crude oil but also sell refined products. Both crude and refined product prices are volatile even in the short term, and correlation among these prices, especially in the medium and long term, is high, though not perfect. This implies that by means of refined product prices, refiners are able to transfer downward in the energy markets a part of the risk they incur from crude oil inputs; the rest of the risk is borne by themselves. By measuring refiners’ risk using a VaR approach, a significant risk reduction can be demonstrated, which is also dependent on the refining technologies being utilized.9 Anyway, the amount of risk borne directly by refiners is not trivial. In fact, the refining margin, which is the difference between product prices and crude oil prices, has always been quite volatile, as shown in Figure 14.4. In the oil industry, there also exists a significant amount of contractual risk transfer, meaning that the transfer between operators is decided among themselves from the beginning and set in formal contractual terms. An example is the netback pricing scheme, where the price of crude oil bought by the refiner is set, using deterministic or semi-deterministic formulae, on the basis of refined product prices. This obviously reduces risk for refiners by transferring product price risk to oil producers instead of crude oil price risk. At the same time, this is also a good example of risk going upward in the supply chain if the oil producer is switching from fixed prices to netback pricing.10 Nevertheless, it should be noted that even in the case of netback pricing, the amount of risk entering the midstream, due to oil product price volatility, will remain the same. Unlike oil, natural gas is consumed as it is and only needs to be transported from gas fields to consumers. This primary energy source has two main destinations: households for final use (especially space heating) and power generation as a fuel input. Producers mostly sell the gas to


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14.4

Quarterly benchmark refining margins of a theoretical cracking refinery in the NWE area processing Brent crude oil, 1992–2004

Source: BP (2005)

intermediaries and traders, very often through long-term contracts. Gas prices are mainly set, at least in Europe, using algebraic formulae whose inputs are prices of other primary and secondary energy sources: P = f (E1, ... ,En),

(14.1)

where P is the gas price and the Ei are energy input prices. The real formulae usually contain additive and multiplicative terms. There are always time lags for the calculation of average energy input prices, consequently reducing short-term gas price volatility. Moreover, energy input prices are generally expressed in US dollars, so exchange rates are also included in the formulae. Finally, these formulae are never used to calculate prices every day but over longer intervals, say, every one to three months, and during this time, prices remain fixed at the most recently calculated level. Each first partial derivative in the formula will express the sensitivity of the gas price to energy input prices; they are normally positive and constitute a distinct feature as they change from formula to formula. Very often, the energy input prices in the formulae are crude and refined product prices. The price European importers pay for natural gas

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is determined by a formula set in a long-term contract, and they are usually able to sell gas to customers (power generators, firms, and households) according to a very similar formula, to which they add a fixed mark-up. The result, which is often referred to as a cost “pass-through,” brings a nearly perfect contractual risk transfer and residual risk being close to zero.11 This long-established practice is welcomed by producers, as gas is often extracted in association with crude oil, and also by gas traders because this link will always assure that gas prices are competitive against the two main available substitutes, which are fuel oil in the electricity industry and heating gas oil in the households market. It is worth stressing that this is the current situation mainly in continental Europe, where liberalization in the gas market is a recent development and far from complete. The United Kingdom, however, began gas market liberalization well in advance of the rest of Europe. Many years after liberalization, we can say that the gas price is determined by the crossing of demand and supply curves, and so a competitive market sets the price of spot and future delivery of this energy source. This is probably the first case where we see the beginning of the abandonment of the oil formula pricing practice, and today, it is a quite common belief that UK gas prices are de-linked, or decoupled, from oil prices, eventually constituting an autonomous source of risk.12 Nevertheless, the UK case shows that for the most part, decoupling is only active in the short term (days or weeks) and is less frequent when prices are observed on a longer time scale. In order to appreciate this point, it suffices to consider Figure 14.5. According to Figure 14.5, the price of gas exhibits seasonal variations in the short term but is driven by oil price trends in the medium to long term.13 Even in the oldest liberalized market in Europe, although the natural gas price broke the deterministic dependency structure implicit in equation (14.1), it is not true that an independent internal source of risk was created in the medium- to long-term horizon. On the other hand, in the short term, we have seen the emergence of an autonomous risk source due to the equilibrium between demand and supply within the gas market.14 UK gas operators use gas trading to transfer these two different risks, which are consequently mirrored in gas prices. This trading activity was fostered by the creation of the National Balancing Point (NBP) gas hub in the UK. A hub is a physical or virtual marketplace where actors can freely exchange energy. At certain times the retrading ratio for the NBP hub, the number of times gas is traded among operators before physical delivery, was more than 15. This means trading


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14.5

Monthly average prices of Brent crude and UK natural gas, 1997–2004

Source: Platt’s Oilgram Price Report and Heren Report’s European Spot Gas Markets.

activity is quite liquid as operators can easily change their exposure to the gas price, and hence the presence of the hub is facilitating the transfer of risk. The overall amount of risk in the system is neither reduced or increased, however, as the general rule is that risk transfer modifies the identity of operators bearing such risk but not the quantity of risk. Always bearing in mind the market structure of Figure 14.3, we now turn to the analysis of coal, which is another relevant primary energy source. Coal, like natural gas, does not need to be transformed after extraction and before final use. Coal is the most utilized primary energy source for power generation worldwide. Traditionally, prices used to change in the medium to long term, as shown in Figure 14.2. Recently, there has also been a wide spread of daily indices, and now coal prices are even showing daily volatility.15 The bulk of this volatility is again entering the European energy markets. In order to understand whether and how it is transferred downstream, it is worth taking into consideration the power generation industry and electricity markets. Figure 14.6 shows the presence of a strong relationship among coal prices and the price of electricity traded at the European Energy Exchange (EEX), the German power exchange.16 This is interesting but not surprising

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14.6

Monthly average prices of electricity exchanged at the EEX and a coal basket, 2003–2004 (baseload future for the next calendar year)

Sources: European Energy Exchange (www.eex.de) and International Coal Report

as Germany is by far the biggest coal producer in the EU-25, with about 36% share. Coal prices are set internationally, not in Germany, while the German electricity market is mainly a regional one. German power producers are simply transferring coal prices and their volatility downward through electricity prices. Although not in a deterministic way, a kind of fuel price pass-through is again in place, while specific factors related to the electricity market can explain the imperfect correlation even in this case. As in the case of Germany, it can quite often be demonstrated that prices of secondary sources of energy (such as thermal electricity) are mainly determined by the prices of primary sources of energy. The examples above show that the concept of decoupling is not the be-all and end-all when it comes to explaining risk transfer in the midstream European energy markets. Especially in the medium to long term, we have demonstrated that primary energy prices are the principal driver for other energy prices. It is possible to outline a general framework in order to understand how energy prices are formed. Whenever prices are not controlled or capped by regulators, they are determined starting from two main inputs, following a dependency summarized in the following equation:


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(Energy price)I = βi × primary energy price + αI × idiosyncratic factors.

(14.2)

This formula, even though qualitative in nature, is capable of representing all the different cases we have studied hitherto. It depicts the dependence of the UK medium- to long-term gas price on oil prices but also on short-term demand and supply conditions related to the gas market. These conditions are the “idiosyncratic factors” inherent in the UK gas market. Refined products show a kind of dependence on crude prices which is not perfect but is generally based on a relationship coherent with equation (14.2). Finally, the formula explains the EEX electricity price dependence on coal prices, which is not a perfect or deterministic one. It is worth underlining that equation (14.2) can also explain primary energy source prices, and this is why the dependent variable on the left is generically referred to as the “energy price.” In fact, equation (14.1), which establishes a deterministic functional relationship for gas prices (i.e. a primary energy source), is just a special case of (14.2).17 Finally, the latter even clarifies price behavior within the oil market, for example, prices for various types of crude or crude quoted in different areas, such as northwest Europe (NEW) and the Mediterranean basin (MED). In conclusion, European energy markets are characterized by a central source of risk, primary energy prices, which most of the time should be identified with the oil price. Other energy prices are linked to this central risk via their individual Ei; there are also idiosyncratic risks, which arise from particular features of markets. In the light of this model we should conclude that along the supply chain depicted in Figure 14-3, there is primary energy risk transfer plus the transfer of internal risk in the shape of idiosyncratic risk.18 FINANCIAL RISK TRANSFER The previous section was concerned with the ways in which risk is transferred in European energy markets through price and contractual relationships. It should be underlined that there will necessarily be agents along the supply chain who, for various reasons, are not able to transfer as much risk they would like, leaving them with a certain amount of risk to bear. Economic theory, thanks to the work of the Nobel laureate J. K. Arrow, has demonstrated that this result is sub-optimal and that agents are better off if they can trade financial instruments against uncertain future outcomes in

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all possible different states of the world, according to their subjective risk propensity (see Arrow, 1964). If this is the case, then markets are said to be “complete.” In fact, financial instruments have been developed and are being traded in European energy markets, and risk transfer through these financial instruments is in place and is important. For example, the residual risk that refiners cannot transfer from crude prices into refined product prices can still be finally borne by them but also transferred to financial intermediaries by means of financial hedging operations on single exposures (e.g. a crude oil swap) or combined ones (e.g. crack spreads). As in other areas of the world, this financial risk transfer is operated through both financial exchanges and over-the-counter (OTC) bilateral relationships. European commodity exchanges traditionally are not the largest markets in comparison to all global commodity financial exchanges as only one exchange is included in the world’s 10 largest (see UNCTAD, 2001; Mauro, 2003). The one exception is the International Petroleum Exchange (IPE), where futures and options on Brent crude and gas oil have been traded for many years. The extreme liquidity of spot and financial trading in Brent crude oil has established this quality as a “benchmark” for the bulk of European and many non-European types of crude oil, whose prices are more or less closely linked to that of Brent. The role of this market is crucial as it has allowed operators to effectively manage their direct oil price exposure stemming from either physical oil trading or deterministic formula exposure (i.e. equation (14.1)). Even indirect exposures, deriving from price dependence as per (14.2), can be managed on the IPE whenever the primary energy price input is constituted by the crude oil price. As we have already stated, the primary energy price in equation (14.2) is very often that of Brent crude. In Europe, there also exist other organized and regulated markets, younger and smaller than the IPE, which have national or regional extent. They trade energy goods, essentially electricity and natural gas, for which there is still no single common EU market. Figure 14.7 gives a summary of these markets and their products.19 Most of these markets are focused on the very short term exchange of electricity, essentially being marketplaces where physical demand and supply meet. Many among them are really “pool” auction markets, where bids for supply and demand are first submitted and then fulfilled on a daily basis (Mauro and Sgarioto, 2001, 2002). On the other end, the most important ones (Nord Pool, UKPX, and EEX) also allow operators to transact financial futures expiring four to five years ahead.


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14.7

Principal non-hydrocarbon exchanges in Europe

Sources: Internet sites of the various exchanges

Recalling the general finding summarized in equation (14.2), it is crucial to stress the fact that as far as electricity and gas are concerned, idiosyncratic factors are very important in the determination of prices. These specific elements are constituted by all the non-forecastable events affecting supply and demand in the short term; regarding the demand side, weather conditions are probably the most relevant. Consequently, it is argued that the role of these European financial markets is highly relevant as they are currently letting operators transfer idiosyncratic risks, and such risks are not addressed and intermediated by the IPE Brent futures. They are thus helping to make markets more complete in Arrow’s sense. It is difficult to assess the extent and instruments traded in OTC financial markets,20 together with the volume of their trading activity. Generally, they act as intermediaries for price risks, on a one-to-one contractual basis, which are not directly addressed in organized markets, through financial instruments that are generally less standardized than those that are exchange traded. Nevertheless, European energy OTC markets indisputably also play an important role in risk transfer. For example, in the OTC segment, agents are able to transfer risks related to crude oils other than Brent or located and traded in different

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places. OTC markets also intermediate a large proportion of oil refined products risk for both the NWE and MED areas. Again, even these OTC oil financial markets often address idiosyncratic factors which determine the prices of these commodities. In fact, there are no regulated financial markets for crudes and oil products of these kinds, with the single exception of NWE gas oil traded on the IPE. For the same reason, the bulk of financial instruments on coal delivered in NWE are traded in the OTC market. The OTC markets’ role is also important in the European electricity and gas markets. Specialized publications, such as European Power Daily from Platt’s and European Spot Gas Markets from Heren Energy, give daily price assessments for electricity and gas OTC spot and forward deals. These deals are more and more frequently agreed between operators in many European hubs and are often intermediated by brokers.21 Moreover, the rapidly developing OTC electronic marketplaces are not concerned with physical location and can efficiently create trading liquidity. Probably the most successful example is the Intercontinental Exchange (ICE), which took over the IPE and currently also lists European petroleum products, crude oil, electricity, and natural gas. These European financial markets, both organized-regulated and OTC, are contributing to an increase in market completeness, but due to their still limited number, the market as a whole is far from complete in Arrow’s sense. Consequently, it is nowadays inevitable that prices are the means used to transfer many aggregated idiosyncratic risks, as per equation (14.2). In light of this, it is thus possible to define European markets as imperfectly aggregating markets. This current state is not efficient as operators are seeking to hedge some states of the world, but in so doing they are unwillingly exposed to new risks. A significant example of this is constituted by weather events. In fact, weather risk is currently mainly transferred and intermediated in bulk within electricity and gas prices, so bundled with other types of risk, i.e. infrastructural risk, regulatory risk, etc.22 It is obvious in any case that at the present stage of development, markets have to aggregate and intermediate different idiosyncratic risks in order to attract sufficient liquidity and develop further. Market specialization will come in a further maturity phase. This development can already explain what will be the main future driver of competition among markets. The winners will be those better able to address the idiosyncratic risks which are more important for operators, successfully removing them from existing aggregated prices. This will imply the identification of standard values for the relevant features of the


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good and the creation of a market structure as close as possible to perfect competition. These processes are often identified as the commoditization of a good. Nowadays, this development is evident in carbon dioxide emissions trading, where existing markets added financial products based on CO2 allowances, as reported in Figure 14.7, and they are now struggling to win market shares. Emissions trading will be discussed in the next section. Finally, we should highlight the fact that financial markets are not and never will be a panacea allowing risk transfer in every case as sometimes only price and contractual risk transfer will remain viable. A typical example is constituted by airlines, which are to be included in the “industrial final users” category at the right-hand end of Figure 14.3. The price of jet fuel, which is a refined oil product, is a large part of their operating costs. However, because of their often considerable credit risk, airlines are not allowed to enter into hedging operations. They can only use their pricing power, transferring fuel costs and risks to their final prices, much of the time explicitly through so-called fuel surcharges. THE IMPACT OF POLICY AND REGULATION Energy sector regulation, together with broader industrial and environmental policy, has a considerable impact on market structures and actors’ behavior. Policy and regulation were probably the main drivers for market liberalization and often fostered or slowed down the commoditization of energy goods. They can have a pervasive impact on risk creation, especially regarding internal risk sources, on downward and upward risk transfer, and on the choice of the subjects that will ultimately bear risks. Examples of regulatory impacts in the European energy markets are legion, spanning the range from short-term measures to long-term structural policies. We recall the example of Italy, where in 2002 the government blocked electricity tariffs, which were usually set as a pass-through formula in line with equation (14.1), thus transferring price risk upward to electricity producers. In 2001 the European Commission approved the EDF-EnBW transaction, subject to the granting of access to generating capacity. Consequently, Electricité de France began to make electricity capacity available to third parties through virtual power plants. This contractual relationship assures the possibility of taking delivery of electricity without bearing fuel price risk and operational risk.23

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Environmental policy can potentially impact every industry in the picture outlined in Figure 14.3 as it is generally empowered to decide pollutant emissions levels from factories and energy products quality specifications. For example, the refinery industry has been affected by both these aspects, considering that in the past years, much attention had to be devoted to lower emissions from refineries and to improving refined products’ quality. The latter increased refining industry risk as legislation forced the introduction of new cleaner products, which are less commoditized and whose prices are consequently more volatile.24 Another impact on the refining industry came from the banning of single-hull oil tankers in European ports, which restricted the number of available vessels, again tightening the market and increasing price volatility. Probably the best example of internal risk creation, new market introduction, and risk transfer principally due to public policy is the case of the EU Emissions Trading Scheme. In 2003 the European Union announced that CO2 emissions trading would begin in 2005, before the start of the first Kyoto Protocol commitment period in 2008. The scheme is a cap and trade system, based on the initial allocation of emission allowances to companies, which then would have to meet their limits by either reducing CO2 emissions or acquiring emissions rights from other companies. This new market is interesting from a theoretical point of view as operators are not exchanging a good but a negative externality from production which has been transformed into a commodity by policy makers. The extent is bigger than the energy market, covering more than 12,000 industrial installations in the EU. The scheme is having a significant impact on operational costs and on the quantity of risk in the system, which has increased at a stroke. From the beginning of December 2004 to July 2005 the price of a permit to emit in the year 2005 one metric tonne of CO2 increased from E8.50 to more than E29. Recalling the general scheme of Figure 14.3, it is clear that the quantity of risk not stopping along the supply chain will generally end up with final users. They are the final destination of the big contractual risk transfer actually in place in the European energy market. If final energy users are other enterprises, they can transfer at least a part of the risk further downstream through prices, depending on the kind of goods they produce.25 On the other hand, if final users are households, they cannot transfer risk further downward, and consequently, energy price fluctuations will affect their available income and thus their ability to buy other goods or to save. Consequently, the bulk of the risk in the system is borne by subjects who


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are not professionals and do not have access to financial energy risk transfer. But here again we find the impact of policy and regulation, as in many EU member states, there exist price protection mechanisms favoring and protecting households, continuing to foster the so-called public service view of energy goods (see Mauro, 1999). This implies that price risk is again moved upstream to energy suppliers. It is important to underline that this practice favors the reduction of energy price consciousness by households, which is a highly undesirable objective for energy policy. In order to give the full picture, mention must also be made of the repeated EU efforts to move price risk further upstream, that is, to agents outside the European energy system. For example, external gas producers were asked to change the terms of long-term contracts, which define the gas price through oil indexation as in (14.1), with the aim of reducing imported price risk. These efforts had no significant effect in recent years. The examples introduced in this section have highlighted some of the main objectives that are currently driving European energy and environmental policy makers: environment protection, further liberalization of energy markets, protection of final domestic customers, and increased commoditization of energy and environmental goods. If these goals are clear, timing of interventions is very often uncertain, real intervention can be incoherent, and policies among EU member states are often not coordinated. These are important aspects of what is commonly referred to as regulatory risk, which is seriously affecting the amount and the ways in which risks are transferred, generally reducing the use of financial transfer while indirectly favoring price and contractual risk transfer. In the final analysis, risk transfer is limited and sub-optimal, implying that along the supply chain, there are operators bearing more risk than they would ideally like. CONCLUSIONS The current extremely high oil price environment clearly shows the problems caused in the EU-25, and in many other developed countries, by high dependency on external energy sources. This situation is surely due to poor natural resource endowments, but it has been exacerbated by society’s attitudes and by politicians’ decisions, or lack of decisions. Fossil fuels were favored for decades only on the basis of their presumed lower economic cost. Other issues, such as environmental costs, social costs, and risk exposure, were largely ignored. In this chapter we have shown

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that the latter element was and remains important and should be carefully considered in order to see the full picture. Politicians have only considered the cost issues and have not seen the risk aspect of energy sources. The European economy was exposed to extreme fossil fuel price risk, and the inertial approach was essentially maintained, even after the two oil shocks of the 1970s. The low perception of the risk part of the story implied that energy costs were not corrected for risk. From this point of view, renewable energy sources were totally neglected as they could not compete with fossil fuels on a cost-only basis. If price risk had been included in the economic valuation, renewable sources could have been competitive a long time ago. Nowadays, it seems more likely than in the past that European energy policy will try to bring about a reduction in dependence on fossil fuel sources. Any gradual switch to renewable sources will have an impact on the market structure depicted in Figure 14.3, on the relative importance of the two factors in equation (14.2), and generally on risk creation and risk transfer. In fact, renewable sources (wind, hydro, solar, etc.) are mainly linked to local and regional elements, and hence idiosyncratic factors will increase in importance. At the same time, the reduction of external energy dependence will move risk from outside import to internal creation, although it is difficult to assess whether the total amount of risk will increase or decrease. Along the supply chain, this will force changes in the way risk is transferred among operators. If, as seems likely, energy markets continue to liberalize, risk transfer through freely agreed prices will increase. The increased future relevance of renewable energy sources, together with the forecastable tendency to further financialization and commoditization in energy markets, imply also that the development of regional idiosyncratic financial markets will continue and that they will address new sources of risk. Consequently, non-energy actors will also be attracted into these markets, as agricultural operators were into weather derivatives, and this will foster market liquidity. The essential life of these financial markets will also be guaranteed by the pervasive introduction of technologies. The development of risk creation and transfer in European energy markets can now only partly be envisioned. Surely the role of policy and regulation will continue to be crucial as they heavily influence agents’ behavior and the development of markets. As this chapter has shown, price risk creation and transfer are complex and important factors. It is desirable


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that policy makers take these aspects into account when producing new regulations in order to avoid the errors and under-estimates of past decades. REFERENCES Arrow, J K (1964) The role of securities in the optimal allocation of risk-bearing, Review of Economic Studies, 31(3), pp. 91–6. BP (2005). BP Statistical Review of World Energy, June. Energy Information Administration (2002). Derivatives and Risk Management in the Petroleum, Natural Gas and Electricity Industries (Washington, DC: EIA, US Department of Energy, October). Energy Information Administration (2005) International Energy Outlook 2005 (Washington, DC: EIA, US Department of Energy, June 2005). Eurostat (2005) Energy, Transport and Environment Indicators (Luxembourg: Eurostat). Mauro, A (1999) Price risk management in the energy industry: the value at risk approach. In Proceedings of the 22nd Annual International Conference, International Association for Energy Economics. Mauro, A (2003) Le borse delle merci, The Independent Review, no. 4. Mauro, A and Sgarioto, R (2001) The valuation of Italian generation assets using a spark spread option model. In Proceedings of the Eni Gas & Electricity Forum, Milan, June. Mauro, A and Sgarioto, R (2002) Verso il Pool elettrico: un nuovo metodo di valutazione delle centrali, Rivista Energia, no. 1. UNCTAD (2001) Overview of the World’s Commodity Exchanges (Geneva: UNCTAD Secretariat).

NOTES 1. Market structures for primary energy sources are quite different among different energy types. For example, the oil market is quite complex, and economists have created ad hoc oligopolistic models in order to explain actors’ behavior and price dynamics. 2. For an analysis of the factors giving rise to daily price volatility in energy markets, see Mauro (1999). 3. In Figure 14.2, coal is represented by marker price (basis North West Europe (NWE)) CIF, crude oil is Brent dated FOB, and natural gas is European Union CIF. 4. Figure 14.3 shows only those parts of the European energy system which we will address in this chapter. Figures are based on data reported in BP (2005) for the year 2004. Figures in italics are limited to principal EU countries as the EU-25 data is not reported in BP (2005). 5. This is true as far as direct price risk transfer is concerned. There may also be indirect transfer through final good prices (cf. final industrial uses at the right end of Figure 14.3). This possibility is mainly linked to market structures and producers pricing power, meaning the possibility of transferring to product prices the increase in costs. For example, industries such as construction and luxury goods have more pricing power than steel, chemicals, and paper.

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6. Figure 14.2 also shows a tendency for prices of different fuels to move together in time. This is often ascribed to another phenomenon known as inter-fuel competition, which can easily be explained by observing that three different energy sources— natural gas, fuel oil, and coal—will “compete” in order to supply fuel for thermoelectric power, as represented in Figure 14.3. 7. Suitable price risk measures should take into account statistical probabilities of price changes using a value-at-risk (VaR) approach. For applications of VaR to the energy industry, see Mauro (1999). 8. FOB stands for Free-on-Board, CIF for Cost-Insurance-Freight. The freights rates market has experienced the biggest price increases in the recent years. For example, BCI route 4 (dry cargoes from South Africa to northwest Europe) opened in 2003 at about $10 per tonne and closed the same year at over $26 per tonne. 9. The amount of risk finally borne by refiners is about half what they would bear if they just bought crude without selling refined products. This is demonstrated in Mauro (1999). 10. It is also frequently the case that oil producers directly enter the midstream sector by building or buying stakes in refineries, again changing their original risk profiles. 11. Note that this contractual risk transfer is not limited to price risk but also affects many other aspects, from volume risk to legal and infrastructural risk, which are addressed by specialized clauses. For example, the typical take-or-pay clause obliges the buyer to pay, even if he does not take delivery of the gas. 12. This does not necessarily imply the complete abandonment of a formula structure similar to equation (14.1). Formulae linking gas prices to electricity prices and the Purchasing Power Index are reported in the UK, stemming from strong competition and volatility in the domestic electricity market. This also underlies the transfer of pricing power from gas traders downward to gas users and risk transfer upward from gas users to gas traders. 13. This claim relies on graphical analysis only and is not based on statistical analyses such as cointegration or Granger causality tests. 14. The UK gas market was probably in the best position, of all EU member countries, to de-link the gas price from the oil price in the short-term horizon as it was mainly relying on internal gas production to meet domestic demand. 15. This phenomenon is mainly linked to the surge in demand and international trading. In just two years, from 2003 to 2005, world coal trade rose about 15% (Energy Information Administration, 2005). 16. See note 13. 17. Even if equation (14.2) was stated in simple additive terms, it could be generally stated as (14.1), without specifying a precise functional formula. 18. The model for energy prices in equation (14.2) is an application to energy markets of the Capital Asset Pricing Model, which explains stock prices as dependent on a common risk factor, the market portfolio, and other risk sources that are specific to the single stock. 19. Updated at the time of writing. As far as the gas market is concerned, UK gas futures are traded at the IPE, while spot gas for UK and Belgium are traded at the APX. 20. It is even difficult to clearly define what an OTC market is and to distinguish it from an organized exchange; on this topic, see Energy Information Administration (2002, p. 48).


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21. Platt’s assesses daily spot and forward electricity prices for the UK, Germany, Austria, Switzerland, France, the Netherlands, Belgium, and Spain. Heren publishes gas prices for the UK NBP, the Zeebrugge (Belgium), Bunde (Germany), and the Title Transfer Facility (German–Dutch border) hubs. 22. In the last few years, weather derivatives trading has been developing, mainly in the OTC segment, and focused on temperature risk. Attempts to introduce exchangetraded products have been made by the London International Financial Futures Exchange. 23. Another example comes from the tolling scheme, where an agent, called a “toller,” is entitled to provide fuel to a power station and can take delivery of the electricity generated, thus directly bearing all the price risks. Banks often require such a scheme to be in place in order to finance the construction of a power station. 24. This development also shows that there is a kind of life cycle in the commoditization of goods. Starting from an initial introductory phase, a growing quantity of the good will comply with the features of a specific commodity, arriving at a maturity phase, which at some point will be stopped by the introduction of new standards and new commodities. 25. See note 5.


C H A P T E R

15

Energy Options 101 Nedia Miller

INTRODUCTION Historically, times of geopolitical uncertainty are usually marked by high commodity prices. The difference currently is that since 2004, prices have been extremely volatile, and the price of crude has reached a historic high. There has been the notion of “peak oil” (that global oil resources have come to their peak and are now heading to depletion) for some time, but this alone cannot explain the current situation in the oil/energy markets. The sophisticated investment houses now perceive commodities in a much better light. They are no longer seen as “boring instruments.” Recent studies show that commodities offer diversification and can be used as an inflation hedge when invested in an equity/bond portfolio. Another historical perspective was that all commodity prices went up simultaneously, and this was explained by the basic economic theory of supply and demand imbalances (when demand increases, price increases, as long as the supply remains stable; to achieve equilibrium again, we have to increase supply). In the case of crude oil, this is a long and expensive cycle. In order for supply to meet demand again, refiners and exploration companies have to build new refineries or invest in oil exploration. These and other efforts are expensive and take years to accomplish. At the time of this writing, companies are making high profits as oil is above $60 per barrel. However, these companies are not investing in oil 231

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exploration; rather, they are buying back their shares instead. Under these circumstances it is questionable whether equilibrium in energy markets will be reached any time soon. According to commodity trade finance statistics, it is estimated that about $1 billion in commodities futures contracts are trading each month, making them more attractive to investors than private equity because of the ease of purchase and sale. This encourages hedge funds to participate in the commodity markets using commodity futures and commodity options in order to take advantage of the current high level of commodity prices. Macro hedge funds are also giving their portfolios an energy-related emphasis. These newcomers are one of the largest holders of energy debt and are major players in the emerging field of “green products,” which are organic and renewable energy sources, but it is not fully clear if they understand the different nature of commodities in general versus the financial markets. High prices and high volatility alone are not a guarantee for high profits. Although commodity derivatives and commodity hedging in general are similar to equivalent activities in the asset classes, there are also significant differences. The major differences are driven by the physical characteristics of the commodity markets as follows. ●

●

●

Commodities are real assets that are produced and consumed in an industrial or other process. In contrast, other interest rate, currency, or equity instruments represent financial claims on different aspects of real assets. In general, commodities have a non-standard structure. This reflects the heterogeneous nature of commodity production in terms of quality and grade. This contrasts with other financial assets that are homogenous. In terms of costs of production, commodity prices frequently gravitate toward cost of production. This is because the market will adjust over time. Additional complicating factors include limit on supply and consumption that is not purely price-dependent, timing delay in production and consumption, and direct exposure to a variety of exogenous functions (such as weather and additional costs, including storage). Another important factor is the opportunity to generate significant value from the shape of the forward curve which frequently exhibits backwardation versus contango (Figure 15.1). For contango, current prices are lower than futures, and for backwardation, current prices are higher than futures.

CHAPTER 15 Energy Options 101

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15.1

Backwardation versus contango

Source: Miller CTA

It is important to mention that particularly in the oil markets, the forward curve is usually in backwardation over very long periods of time. Moreover, it has been observed that the intensity of the backwardation of the energy forward curve increases as the price for all maturities rises. In addition, because arbitrage relationships between the futures and the physical market are limited, price volatility is positively correlated with the degree of backwardation. For the newcomers in the energy markets we will review some popular market structures used by market participants. BASICS OF OIL DERIVATIVES STRUCTURES (EXCHANGE TRADED AND OVER-THE-COUNTER) Energy options and over-the-counter (OTC) derivatives were originally created for risk management purposes. Therefore, we will first have to take a brief look at the interdependence of specific energy industry–related risks. If we look at the risk matrix, we see how all risks are interrelated, and the relationships between them are at least three-dimensional. It also becomes clear that it is impossible to manage one risk effectively without taking into consideration all the other risks (price risk, credit risk, liquidity risk, cash flow risk, basis risk, legal risk, tax risk, and operational risk). Oil price risk is the risk of losing money as a result of price movements in the energy markets (sometimes also referred to as market risk).

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Usually, producers will lose money when prices fall, while users will lose money when prices rise. Liquidity risk has a strong influence on energy prices. This is the risk of losing money when a derivatives market becomes illiquid. This situation occurs when there is so much volatility in the market that many banks and oil traders will not give a bid or offer price. Lastly, basis risk also relates to price risk. This relationship is key in the energy derivatives markets and should be understood fully by newcomers (non-commercial players) to the oil markets before investing. Basis is the relationship between the price of the futures contracts and the price of the cash commodity being hedged. The basis changes over time because cash and futures do not move in perfect sync. Changes in basis impact hedge performance because a narrowing of the basis benefits a seller’s hedge short hedge; a widening of the basis benefits a buyer’s long hedge. In summary, the decision to hedge a physical transaction is based upon the expectation that price changes during the life of the transaction may have a negative impact. BASIC DERIVATIVES STRUCTURES According to the Sarbanes–Oxley Act, crude oil and petroleum product derivatives for both exchange-traded energy futures contracts and forward contracts are considered derivatives and are accounted for accordingly (FAS 133). Energy futures contracts are legally binding standardized agreements on a regulated futures exchange to make or take delivery of a specific energy product at a fixed date in the future and at a price agreed when the deal is executed. Traders in commodities have used futures markets for hundreds of years. On the New York Mercantile Exchange (NYMEX), the world’s largest regulated energy futures exchange, futures contracts settle on expiry and require physical cash delivery on expiry. In contrast, OTC derivatives are cash/money settled by the very basis of their legal construction under International Swaps and Derivatives Association master agreements. Therefore if the seller (who is short in the market) holds the futures contract to expiry, he will have to deliver the underlying physical energy commodity. Exchange traded, listed options on NYMEX are: ● ●

WTI crude oil options; #2 heating oil (gas oil) options;


● ● ●

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unleaded gasoline options; natural gas options; electricity options.

In the futures markets, the standardized options contracts are calls and puts. Calls give the right but not the obligation to buy the underlying futures contract at the strike price. Puts give the right but not the obligation to sell the underlying futures contract at the strike price. In the exchangetraded options markets, instead of participating in fixed price buying and selling levels like a futures contract, there is a strike price and premium. Strike price is the level at which the trader/investor begins to participate and benefit from the price move of the underlying market. OTC-traded options are caps and floors (Figure 15.2). Buying a cap gives an upside protection at a fixed premium on the assumption that the market price will become stronger. A buyer of a floor buys downside protection at a fixed premium on the assumption that the market will become weaker. The options products that are most frequently used in the OTC market are caps and collars. A collar is often constructed at “zero cost”—it is the simultaneous buying of a cap and selling of a floor. This structure is very popular among end users as there is no upfront cost. Other popular OTC structures are price swaps contracts that are transactions where two parties accept to exchange fixed payments against

F I G U R E

15.2

Caps and floors (call/puts)

Source: Miller CTA

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floating payments. The floating payments are generally linked to an average of fixings over a period of time, usually a calendar month. They are similar to futures contracts, except that terms do not have to be standardized. Swaps usually engage in direct counterparty trading and are executed directly to the counterparty or via brokers. Simple oil swaps are agreements whereby a floating price is exchanged for a fixed price. For example, in order to manage price exposure to oil price fluctuation, an oil producer enters into an oil swap to lock in a fixed oil price (WTI crude oil). The opposite side is the risk profile of the oil consumer who is looking to lock in a fixed price for oil purchases for a certain period of time. Both parties agree to exchange cash, whereby the oil producer receives a fixed price on a pre-agreed volume of oil and agrees to pay a floating price index (WTI index) on an identical volume of oil (100,000 barrels of crude WTI to be delivered each month for a specific period of time, i.e. 6 months). The oil consumer enters into the reverse part of the same transaction. The counterparty (the consumer) agrees that settlement will be in cash based on WTI prices on a monthly basis. The transaction allows both counterparties to lock in a price on an agreed volume of oil off the WTI Price index.1 It is important to mention that pricing of oil swaps is based on the decomposition of the transaction into a series of crude oil forward contracts. The deviation of the value of the oil swap is based on combining a series of forward prices. (The details of the procedures of the documentation and of combining a series of forward prices are beyond the scope of this chapter. The above example is meant to give a basic understanding of the strong impact of the specifics of the underlying physical oil market for pricing not only swaps, but all OTC derivatives structures.) Another frequently used instrument is the differential swap that is based on the difference between a fixed price in two products. For example, the jet fuel gasoline differential swap is a popular instrument. It is also known as “regrade” swap. Figure 15.3 shows a plain vanilla swap transaction. Refiners who prefer to fix a known refining margin can construct complex hedges (such as margin/crack swaps) to protect their product output and feedstock prices. However, it can be easier and more costefficient to enter into a margin swap with another counterparty where oil products are being produced and crude oil/ feedstock is automatically hedged.


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15.3

Types of swaps contracts

Source: Miller CTA

Among the OTC instruments, Asian options are now more frequently used by market participants. This is because Asian options are less sensitive to the extreme market conditions that may prevail on the expiration day (due to random shocks or outright price manipulations) due to the averaging effect. The majority of Asian options in crude oil are traded over one-month averages. The profit of the Asian option depends on the price history of the underlying energy commodity that is being used as the price reference, over all or part of the life of the option. Therefore these options are also called path-dependent options. Asian options fall into the category of exotic options. Another OTC option that falls into this category is the barrier option. Barrier options were invented to reduce the initial cost of hedging with the buying of options. The barrier option either comes to life (is knocked in) or is extinguished (knocked out) under certain conditions. In practice, the event that activates or kills the option is defined in terms of a price level (barrier). Barrier options may be combined with rebates; for example, the knockout is paid when the option is cancelled as compensation to the holder. Barrier options are typically purchased by energy producers to hedge their natural long position in the markets. An up-and-down floor (put) may be an attractive alternative to the normal floor/put option as it is less expensive and provides the same price protection if prices move down from current levels. However, if prices move upward, the increase in the underlying commodity’s price reduces the need for downside risk protection at the original strike price. If the price moves up sufficiently to cross the selected barrier price, then the option is cancelled/extinguished. Figure 15.4 shows a barrier option strategy.

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15.4

Barrier options (caps/floors)

Source: Miller CTA

OPTION PRICING METHODOLOGY/OVERVIEW OF OPTION PRICING In order to understand how energy options are priced, we will start with a brief review of the basic option terminology. ●

●

●

●

●

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●

Strike price. The fixed price (as specified in the options agreement) at which the option call/put is being exercised, known also as the exercise price of the option. (For exchange-traded options, the strike price is set in intervals.) Premium. The price of the option, or money received when the option is sold. Intrinsic value. The difference between the strike price and the current market rate. Time value. The life of the option, which is equal to the difference between the option premium and the intrinsic value, including time until expiry, volatility, and cost of carry. Fair value. The combination of intrinsic value and time value, as calculated by the option pricing model. Value date. The date when the underlying energy commodity is settled. Volatility. Comes in two varieties, normalized and implied. Normalized volatility is the annualized standard deviation of the underlying futures/swap market contract. Historical volatility is determined from past price data by selecting the appropriate


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time period. Implied volatility reflects market perception of future volatility. It is not historical volatility. Implied volatility value is always placed on option quotes. Implied volatility is a key element of option pricing. Table 15.1 a simplified example of option premium calculation (principles – premium). Popular Option Pricing Models Used to Price Energy Options The most popular option pricing model is the Black–Scholes option pricing model adjusted for pricing commodity options, developed in 1973 (see Figure 15.5). It is used predominantly for options on futures and European options and, with some modification, for pricing American options. The basis of the model is to estimate the probability that the option will finish in the money. The model assumes that the price of the option is related to the square root of time. Another key assumption is that price volatility is at a constant level and can be measured through the standard deviation of historical prices. However, these assumptions make the original Black–Scholes model inappropriate for pricing energy options. There are specific issues in energy options (options volatility in all energy markets is not stationary across options strike prices) which make it difficult to price them, and therefore the model needs to be adjusted to accommodate alternative assumptions. In addition, Black–Scholes assumes a lognormal distribution of prices, which is not the case in the energy markets, which are skewed by the price spikes of the underlying energy commodity. T A B L E

15.1

Brent crude oil options Calls, with underlying @ $28.99

Exercise Price

Current Settlement Price

Implied Volatility

Open Interest

$28.50 $29.00 $29.50

85 cents 57 cents 37 cents

32.52% 31.93% 32.49%

440 993 201

Source: Miller CTA

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15.5

Black–Scholes model

Source: Miller CTA

The adjusted model must presume deterministic and random price components, which follow mean reversion to reflect seasonality features and others. This options pricing model is oversimplified for practical reasons, and even after adjustment its accuracy for pricing options in the energy markets remains questionable. Another method for pricing energy options uses the Monte Carlo approach. This approach was first suggested by Boyle in 1977 in the Journal of Financial Economics and provides a method of solving certain numerical problems by using a number of random samples to estimate the true value of a quantity. A number of other scholars have taken the basic concepts of the Black–Scholes pricing model and developed it further. One example is the Cox–Ross–Rubinstein option pricing model (a binomial model which contains a probability tree; similar to Black–Scholes, but it uses a different methodology). Readers who wish to find out more should consult www.wallstreetmodels.com/Derivatives/Options/ bsFuture.html. Spread and Options on Spreads There is another popular group of structures which characterizes the energy markets. These are the different kind of spreads, which are based on differentials. Some examples are as follows.


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Calendar spread. The spread between the commodity traded in two different calendar months. Crack spread. The spread between the crude oil and two products (heating oil and gasoline) called “paper refinery.” This spread has been extremely popular since the 1980s. Spark spread. The spread in the electricity market between natural gas and power called “paper plant.” This spread was created in the mid-1990s to be analogous to crack spread but did not become as popular as crack spread.

The crack spread typically reflects real-world refining ratios. A popular spread is the 3-2-1 spread that uses the prices of three barrels of crude, two barrels of gasoline, and a barrel of heating oil to determine the spread. Another common spread uses the 5-3-2 ratio. Many other ratios are used as well. Hedging crack spreads with futures locks a market participant into a differential which may require him to relinquish a favorable market move in return for price stability. Crack spread options were first structured in 1987 and they had a dramatic impact on refining margins at that time since they provided the opportunity for refiners to combine spreads with traditional energy futures. The use of crack spreads grew as the price of crude oil and products fluctuated dramatically due to extreme weather conditions in the middle and late 1990s. In 1994, NYMEX created crack spread options as exchange-traded instruments and designed them to help refiners and other gasoline market participants to protect against the changing relationship between crude and product markets caused by factors such as changes in crude supply and product demand, seasonal market dynamics in heating oil and gasoline, changing inventory patterns, and changes in market contango and backwardation. Crack spread options are also designed to protect the refining margin while at the same time allowing refiners and other market participants to benefit from the favorable changes in the spread. A futures crack spread executed on the exchange is treated as a single transaction for the purpose of determining a market participant’s margin requirement. Specifically, the minimum margin requirement takes into account that the risk on one side of the spread is generally reduced by the other leg spread. It is important to mention that crack spread options allow market participants with commercial exposure to tailor their hedge to their price risk without giving up the ability to participate in favorable market moves. Crack spread options, in general,

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furnish traders with an efficient mechanism for hedging and the changing relationship between crude and products and, most importantly, allow refiners to generate income from writing options. Spread options and differential swaps are some of the most common instruments traded on the energy markets. They have a crucial role in most energy portfolios. Yet finding an appropriate model to price those spreads and spread options accordingly remains problematic. Most of the models used to price these instruments make unrealistic assumptions about underlying market behavior. Joint price distributions are lognormal. Spreads are potentially unbounded and use constant linear correlations to describe the dependence structures between dependent random variables. Each of these assumptions remains questionable. The empirical distributions of most energy spreads show that these simplifying assumptions are inconsistent with real-world energy markets. Therefore market participants often respond to these issues by using different correlations for different purposes; they use correlations for adjustments for time, maturity, different strike prices, etc. However, the fact remains that the two-factor model assumes that the “true” correlation is constant. Therefore correlation is not an adequate measure of dependence and is far from being an all-purpose dependence measure. It has often assumed to be the linear correlation coefficient, applicable only to dependent variables that are subject to elliptical shocks, which applies to few if any energy commodity price distributions. It is clear that although the key element of pricing spread options is correlation and not implied price volatility, the assumption of a lognormal distribution remains a problem in pricing energy spread options as well. Here the problem is assuming constant “true” correlation versus constant volatility across all options strikes (when pricing single energy options). In order to find a better way for pricing energy spread options, some market participants have been experimenting with other methods for pricing these options. They use copulas as an alternative dependency measure that is reliable when correlation is not. This method allows us to measure non-normal processes (such as mean-reverting jump diffusion) and to be able to determine correctly their joint behavior.2 CONCLUSIONS Research is ongoing in the energy-financial industry to determine an accurate options pricing model. Scientists as well as a number of practitioners are


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jointly working on making new adjustments to classical financial options pricing models as well as on constructing new pricing methodologies to better serve market participants. It is important to study the energy markets and the more common market structures by paying attention to the specifics of the underlying energy commodity. This is particularly important for those non-commercial participants who have been recently attracted by high energy prices and high volatility. The energy markets will welcome these newcomers as long as they familiarize themselves with the characteristics of the underlying energy commodity. NOTES 1. The WTI index is based on the closing level of the WTI net futures contract on NYMEX. 2. Copulas allow us to extract the dependence structure from the marginal distribution function from the joint distribution function and so separate out the dependence structure from the marginal distribution functions


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Energy Trading, Transaction, and Risk Management Software: A Key Component in Risk Management Gary M. Vasey

INTRODUCTION For any entity trading energy or any other type of commodity in today’s business and regulatory environment, software is required to manage all aspects of the business, from deal capture, through risk management, to settlement and invoicing. In the energy commodities world, this task is both more complex and more difficult. Indeed, part of a comprehensive approach to risk management demands that systems are in place to adequately support all aspects of the business and its processes. The category of software used to support energy trading and risk management activities is commonly referred to as energy trading, transaction, and risk management (ETRM) software. As anyone who has selected or used ETRM software will know, there are no perfect solutions out there. This chapter will review the current state of the ETRM software market, discuss current trends in ETRM software solutions, and discuss some aspects of how ETRM software impacts risk management. HISTORICAL PERSPECTIVES The ETRM software category has a 15-year history, but it is one that is only now beginning to adequately mature. The progressive deregulation of the 245

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energy industry by commodity at both the wholesale and retail levels has meant that the requirements for such systems have been a moving target. Added to that is that the industry has experienced several crises, such as the California retail market issues and the collapse of the energy merchant segment (Vasey, 2004a). Each of these industry events resulted in restructuring of the industry at the macro and micro levels, the introduction of new regulations, and changes in business models, strategies, and processes. The emergence of the ETRM software category was created with US FERC Order 636 in 1992, which completed regulation of the natural gas industry, which, as luck would have it, coincided with the emergence of client/server technologies for software development. At that time, a number of vendors emerged offering client/server-based natural gas marketing systems. For a few years, the adoption of these products was rapid, even enticing one or two natural gas marketers to bring their own internally developed solutions to market as software packages. However, deregulation of wholesale power caused a dislocation event to occur as natural gas marketers and energy companies generally considered how to become power traders too. Sales of natural gas marketing systems slowed significantly during the uncertainty, and a new requirement emerged for power and gas marketing systems. The new market was larger since it involved a whole raft of utility spin-offs and gained the interest of Wall Street. Wholesale power deregulation was followed very rapidly by the emerging requirement to measure and control financial risk. In this brave new world of energy commodities trading, some of the natural gas marketing vendors were able to evolve and survive (at least temporarily), while others dropped back, content to continue to supply specialized natural gas marketing software into a smaller market (the stranded vendors), and a whole host of new entrants appeared, among them financial risk management software companies that spied their opportunity, energy service companies, and new start-up ventures. The energy industry remains extremely volatile, and requirements can and do change rapidly as a result of regulatory changes or industrywide events. Historically, these abrupt changes in industry direction have resulted in dramatically changed software requirements. The Energy Merchant collapse in 2002–2003, for instance, led to a wide number of knock-on effects that included the need for greater attention to counterparty credit, contract management, the potential implementation needs around the Committee of Chief Risk Officers (CCRO) recommendations and Sarbanes–Oxley (US federal law on corporate governance).

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Just as importantly, each industry event fundamentally changed the direction in which the industry was moving. Prior to the merchant sector crisis, the growing merchant segment of the industry was thriving and growing, and many other energy companies were essentially outsourcing their production marketing, energy procurement, or risk management needs to those merchants. Following the collapse of Enron, producers are taking back the production marketing function; utilities, LDCs, and end users are taking back the energy procurement function; and who is still interested in outsourcing risk management? This fundamental and abrupt change in the business drivers I call a “dislocation” event, and its impact on vendors is discussed in more detail below. CURRENT STATUS OF ETRM SOFTWARE While more than 60 vendors (UtiliPoint, 2005) compete to provide some form of ETRM software, a small number of vendors dominate the market. Many of them have attained this position through the acquisition of other software companies and their products. In fact, there is no single dominant product in the market. And even those products that do have a sizeable installed base, often owned by vendors that acquired them, may be rapidly reaching the end of life. It is no wonder that a large proportion of the industry still relies on spreadsheets. A Heterogeneous Market The energy industry is actually a highly heterogeneous market for software with many horizontal and vertical niches that, on the surface at least, appear to share similar requirements. While many “outsiders” see a large and attractive homogeneous market for ETRM software with good revenue and profit potential, the truth is that there are a plethora of energy company business models, and each model has its own detailed version of the same set of requirements. Indeed, at a certain level of detail, all energy companies involved in buying and selling energy commodities have common business functions. Yet as you drill down, there are significant and fundamental differences in those requirements. The idea that there are standard functionality requirements is simply a mirage created by a fundamental lack of understanding of the energy business at a detailed level.

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Requirements Dictated by Assets and Location The nature of each energy company’s physical assets and the geographic location of those assets actually dictate the majority of the software requirements at the detailed level. The need to record and report on data and transactions is inevitably governed by the regulatory regimes under which the company operates its assets and by the type of assets employed in the business. For example, an electric generator with predominately hydrogeneration facilities will have different requirements from the company that has predominantly coal-fired facilities. Generators in different geographic regions will have different reporting requirements. During the energy trading bubble of 1999–2001, the fundamental importance of assets was overlooked, and the recent return to asset-centric trading has increased the importance of asset-related software requirements. Traditional Software Business Model Energy software vendors largely follow a traditional packaged software business model that requires them to sell more and more software licenses. The presumption behind this model is that a single shrink-wrapped packaged software application has a large enough potential market to support it. The truth is that in the energy industry, this is often simply not the case. Vendors’ products tend to evolve into increasingly complex software as they are sold into an ever larger installed base simply because the vendor has to enhance and modify its software to meet the specific requirements of each new additional customer. The end result is often a near-unsupportable set of spaghetti code. Worse still, in the hands of a poorly capitalized vendor, the problem is further magnified as the vendor has insufficient cash to keep up the support and ongoing enhancement of the product needed to pursue the traditional software business model. There are two fundamental reasons why this state of affairs exists today. First, this is not a traditional shrink-wrap software package market. Second, the industry has not yet evolved to a point of sufficient stability to support a traditional software model. Indeed, the history of this software category demonstrates both these assertions admirably. No Emergence of Mega-Vendors? In spite of the history of the industry and its inherent volatility, anyone looking for ETRM software today, without the benefit of hindsight, could be


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forgiven for believing that two or three mega-vendors may have emerged. While it is certainly true that there are two or three more dominant vendors, one needs to look at the products, not the vendors. At that point one discovers that the dominance came through the acquisition of stranded vendors and products. In other words, the vendor has multiple product platforms, many nearing the end of their lives, none of which are likely well suited to today’s energy company looking for a product. That is not to say that these vendors’ products are not suited to some energy companies but merely that there is no single dominant product in the market. While the energy industry continues to evolve with periods of tremendous demand for software followed by periods of uncertainty and change, no dominant product or vendor can emerge, especially if a traditional package software model is pursued by the vendor. But why do vendors adopt the traditional package model if it does not work? The answer lies both in the economies of scale that can benefit the buyers of packaged software and in the vendors’ need for outside venture capital. Obtaining outside investment is often difficult if an unusual business model is adopted by the vendor and valuations are considered higher for vendors with a high software license revenue component. Integration and the Search for the Solution To be fair to the vendors, many have recognized the significant issues facing them. Buyers too, especially the more savvy variety who had perhaps some experience in the software market, recognized that it was unlikely there would be a single supplier of the complete solution. Both buyer and vendor eyed a “best-of-breed” model where software could be written to perform a particular set of business functions extremely well. For example, energy risk management or scheduling applications could be developed as best-of-breed applications. However, in a best-of-breed architecture a new problem emerges. How can the best-of-breed applications be integrated to build a seamless solution for the entire trading operation? The answer was to deploy middleware. While buyers sought to find suitable suppliers of middleware, many vendors tried to build middleware into their own application suites, thus creating a complete single source solution comprised of best-ofbreed components. At this point, the experience in the industry has been that the introduction of middleware increases project complexity, risks, and costs to an

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unacceptable level for all but the largest of energy firms. It is simply very difficult to achieve a complete set of integrated applications in a business environment that is still so dynamic. For example, as each vendor attempts to keep up with industry requirements for their best-of-breed applications, they are forced to issue several upgrades each year. This presents a considerable problem for the buyer, who is either faced with falling behind on their vendor’s software and, of course, new functionality that might be imperative or continually revisiting the integration framework as they “bolt in” the upgrades. Those searching for a complete set of integrated and customizable software applications are still disappointed. If It’s not a Package Market, then What Is It? To be successful, the vendors themselves have to adopt different business models that essentially allow them to escape the endless cycles of market growth and dislocation. Not only would this provide the industry with vendor and product stability, it might also lead to the emergence of stable, long-lasting vendors that offer what the industry really needs in terms of its software requirements. There are new business models developing that might provide the answers. They range from the use of new software architectures and tools that incorporate connectivity, such as Web services, to models that implicitly recognize the limitations of the traditional packaged software business model. A DICHOTOMY OF REQUIREMENTS Industry structural change following the collapse of the energy merchants has had a significant impact on ETRM vendors and solutions. As utilities, producers, and other holders of physical assets moved to a more assetcentric business model, a new breed of speculative energy traders has emerged in the form of investment banks, hedge funds, and the multinational oils. As a result, there are now really two sets of requirements for ETRM software: one for those who essentially need to manage up to the actual trade (speculative traders) and one for those who have to actually manage the physical side of the business or move the molecule (asset-centric players). While natural speculators may take a risk position based on their understanding and view of the market, the asset-heavy side of the business is more concerned with dealing with their naturally long or short position


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with minimal risk and with maximizing their asset’s profitability. The more speculative side of the industry needs to be able to manage market data, capture and process trades, and engage in sophisticated risk management and market modeling. The asset-heavy side of the industry certainly needs to perform some of those functions but also has to track and reconcile the movement of the molecules. This dichotomy has reflected itself in a wide number of available solutions for trade capture, position keeping, and risk management, and a number of more specialist and niche-oriented vendors focused on logistics, scheduling, asset optimization, generation dispatch, and so on. While a small number of vendors can provide most of this functionality, it is not always best in class, and so most users end up with a number of different applications in place to support their business requirements. Indeed, a recent study of applications used to support electric power wholesale trading in North America found that on average, each marketer or utility was utilizing between 6 and 12 different applications (Vasey, 2004b). This same study found that more than half of the companies surveyed were also using manual interfaces between these applications to manage the business. In today’s era, this is a significant business risk. A NEW ERA OF ETRM SOFTWARE? Today, many of the vendors are moving rapidly to new technology platforms and architectures, such as service-oriented architectures, using Microsoft.net and XML Web services. The benefits for the end user and the vendor of these relatively newly available architectures are substantial, but, perhaps most importantly, they provide the built-in connectivity that allows the integration issues to be solved. In effect, the new architectures being deployed by the ETRM vendors provide many of the following benefits. ●

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They provide connectivity that allows integration with enterprise applications, external data feeds, and applications and provide the basis for constructing a true best-in-class suite of fully integrated ETRM applications. They enhance the scalability of the ETRM software through the easy addition of additional processing power. They provide the basis for the addition of workflow and business process management tools and audit and document management capabilities.

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They allow for enhanced reporting functionality via the addition of a reporting application or using the vendors’ own reporting capabilities. Some of the vendors are now offering drill-down reporting complete with graphing and mapping features. They provide the vendor the opportunity to build in more configurability, allowing the package to be customized for each user’s particular environment and culture, thereby enhancing implementation success rates and allowing the vendor to pursue a traditional software vendor business model more easily. They enhance the vendors’ ability to keep up with industry change by allowing them to break the application up into smaller modules of more discrete functionality. They enhance the support and maintainability of the ETRM application.

There are many additional benefits of these architectures, including the ability to build data marts and data warehouses from which to perform more analysis. As the vendors migrate their current applications to these new architectures and platforms, they will be able to serve their users with more flexible, usable, and customizable but supported third-party software. However, the implication of this migration is that the dichotomy of requirements described above can be met with best-in class-ETRM software suites from a single or multiple vendors. The lack of integration and the risks inherent with that lack of integration seen among marketers and utilities today can potentially be resolved. ETRM SOFTWARE AS AN ESSENTIAL PART OF RISK MANAGEMENT POLICY In the era of Sarbanes–Oxley and with the industry making recommendations on risk management through the CCRO, the management of enterprise risk and emphasis on corporate governance means that proper management of the business process around energy trading is a significant component in overall risk management. Not only do trades and their associated transactions have to be properly recorded, but there has to be an audit trail of all changes made to that data along with a record of workflow. As the ETRM system becomes the “system of record” for much of the trading data, security of access to data and functions also needs to


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be guaranteed by the software. Today, evidence suggests that many companies still over-rely on spreadsheets that are open to abuse, error, and oversight (UtiliPoint, 2005). To comply with properly drawn-up risk management procedures as well as to comply with corporate governance mandates, companies trading energy must have a comprehensive ETRM system in place that provides surety and auditability. Additionally, they must have access to proper functionality that actually supports their business and business processes. This includes adequate reporting tools and the ability to drill down into data intelligently to account for variances, to properly explain results, and to mine for trends that can be used to set up strategies. Today, ETRM software as provided by vendors is approaching this level of requirement, and only an ETRM software solution can provide the basis for complying with internal and external risk policies. Even if imperfect, it is better than the ubiquitous spreadsheets. RISK MANAGEMENT TOOLS AND METHODS Many of the risk management software packages and toolsets were originally developed for the financial industry and as a result do not always reflect the complexity of the energy business. Most energy-oriented risk systems will perform value-at-risk and mark-to-market calculations, and some will also provide stress testing and Monte-Carlo-based value at risk. Where many fall short is in dealing with the physical side of the business in terms of measuring volume and deliverability risk or modeling generation assets or storage. Too often, physical assets have to be modeled imperfectly as an option as work around, for example. However, most risk systems can perform the basics of risk management for energy and provide a level of risk reporting, and most vendors are now moving to address omissions. Despite that, the energy industry is complex, and there will always be situations where proprietary models and approaches need to be taken. The quant will continue to be in demand to help set up models for complex options, real options, storage models, and the like. Once again, this means that ETRM software needs to be fully integrated with proprietary or best-in-class risk management tools and models. The architectural movements outlined above may help in this regard. What is important when using risk management software as part of an ETRM system is to understand just what it can and cannot do.

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Users need to be aware of its strengths and weaknesses and build that understanding into risk policy to ensure that the chance for mistakes and errors is minimized. CONCLUSIONS Despite a history of least 15 years, ETRM software is still maturing as a class of software. Recently, many of the vendors have begun to migrate their software to new architectures that provide for additional benefits for users, particularly in terms of connectivity. Although imperfect, today’s business and regulatory environment demands that some form of ETRM software solution is utilized since it provides some degree of control over other solutions, such as spreadsheets. To properly comply with a comprehensive risk policy, it is imperative to have some form of ETRM system in place that provides at least basic database, reporting, audit trailing, and workflow. The key to success is in understanding the flaws of the ETRM system and ensuring that those flaws are compensated for in the company’s risk policy and procedures. Similarly, it needs to be recognized that many energy risk management software solutions will provide only the basic risk management requirements and may need to be supplemented by workarounds and proprietary models. Again, these weaknesses need to be understood and compensated for with an overall risk policy. Finally, a review of ETRM systems and software should always be an important component in any risk audit undertaken. REFERENCES UtiliPoint (2005) Directory of Trading and Risk Management Software Vendors and Solutions, www.utilipoint.com Vasey, G M (2004a) The History of the Energy Trading, Transaction and Risk Management Software Industry, UtiliPoint White Paper, www.utilipoint.com/reports/ Whitepapers.asp Vasey, G M (2004b) IT Benchmarking at North American Power Marketers, UtiliPoint International Report, www.utilipoint.com

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Electricity Options Bob Kristufek and Jason Oakes

INTRODUCTION Options on electricity, like derivatives on other traded instruments, possess a certain set of basic characteristics which allow them to be combined to form an array of instruments. The value of the instrument may depend upon the price of electricity alone or upon the price of electricity with respect to other assets or indices such as fuel prices, or even degree days for a given period and location. Electric power can be transacted using forward contracts that cover discrete months or seasons, specific hours of flow during that period, and a geographic location. Options on this commodity are commonly traded not just on underlying price but also in baskets of forwards or even relative value among various contracts, groups of contracts, and delivery locations. There are many possible applications of electricity option valuation, covering the risk management spectrum from stand-alone option portfolios to complex portfolios of generation assets and demand liabilities. Since most of what has been written on options has been from a highly theoretical perspective with emphasis on the stock and bond markets, this chapter will attempt to explain the particular nuances of the electricity derivatives from a more useful and practical perspective. An introduction to any option market must start with basic terminology that 255

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is applicable to all option/derivative analysis. These basic definitions are generally the same across all markets, whether one is discussing foreign currency, stock, bonds, or commodity options. Nonetheless, an expert derivatives trader in one market may lack the skills to effectively trade options in other markets. In other words, it is essential to understand the basics of generic options trading and the methods behind the theoretical valuations. However, it is just as essential to understand the intricacies of the particular market where one is trading or applying risk management. This is particularly true when dealing with the electricity markets, as some might argue, due to the inability to store this commodity for future use. This does not imply that any traditional methods of valuing and trading options in other markets become useless, it just suggests that one cannot take much for granted. Electricity is certainly unique among traded commodities. In this chapter, we will cover the development of electricity option markets, option basics that are applicable to the electricity markets in layman’s terms, electricity option valuation and modeling, option portfolio management, and finally, various option structures and their applications in risk management. HISTORY OF ELECTRICITY OPTIONS Electricity options have been used by utilities and end users when entering into power purchasing contracts. However, the hidden optionality on volume and location found in these agreements was not financially acknowledged and traded until the late 1990s with the aggressive growth of many energy trading operations, particularly by electric utilities. The New York Mercantile Exchange (NYMEX) introduced the first electricity futures and options contracts in March 1996 on the COB (California Oregon Border) and Palo Verde hubs. Soon to follow were the overthe-counter (OTC) NYMEX look-alikes imitating the natural gas market, although there was an active OTC electricity derivatives market from late 1993. In 1998 and 1999, the NYMEX added the Cinergy, Entergy, and PJM futures contracts. Additionally, Nord Pool—the oldest and most mature electricity market today, founded in 1991 and discussed in Chapter 5—introduced option trading in 1999. None of these financial power markets have exploded with growth as most power trading is still physically oriented. They were probably most active before the fall of Enron in December 2001; however, with new players such as banks, foreign utilities,

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hedge funds, and demand-side hedgers continuously entering into the market, some growth is now anticipated. WHO USES ELECTRICITY OPTIONS? Option players can be categorized into three major areas: hedging, speculating, and arbitraging. The hedgers include any market participant who wishes to reduce risk to energy prices. These players include producers of electricity who are at risk when prices drop and consumers of electricity who are at risk when prices rise. They may choose to buy or sell options to offset their inherent risk of price movements. A utility that produces power and consumes natural gas may need to hedge both risks and may use a hybrid “spark spread” option to reduce price risk. This topic will be addressed in more detail later in this chapter. Hedgers often use naked plain vanilla options such as buying puts or selling calls or both (collars) to protect against adverse price moves. The speculators include market participants who are using trades to profit from various movements of energy prices. These include hedge funds, banks, and some energy marketing companies. Speculators will buy naked options for directional plays, which give them limited downside (cost of premium) and unlimited upside. Speculators will also bet on the direction of volatility by buying and selling straddles and strangles. The arbitrageurs include those individuals who profit from the inefficiencies of the market. These particular players are few in number relative to other commodity option and derivative players around the financial world. Liquidity is a necessity in arbitrage opportunities. The current lack of electricity option and forward liquidity limits this strategy, unless the arbitrageur can create and successfully use other correlated markets, such as natural gas, that are more liquid to theoretically capture profits with limited risks. These market maker types can accumulate a complex portfolio of positions that may require micromanagement using traditional “Greek” tools and ongoing correlation analysis to minimize risk. The reality of micromanagement not being conducive to illiquid markets creates a disincentive for the risk-averse to participate. OPTION BASICS The basic building blocks of all option strategies are the call and put options. The call option gives the option purchaser the right to buy an

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underlying asset at a given strike price at a certain date. The put option gives the option purchaser the right, but not the obligation, to sell an underlying asset at a given strike price at a certain date in the future. When combined with the underlying asset, a call option will mimic the payout of the same strike put option. For example, buying a January $90.00 ERCOT (Texas electricity grid) call contract while selling a January ERCOT forward contract at $90.00 will have the same payout structure as buying a January $90.00 ERCOT put. This relationship, referred to as put–call parity, is the underlying equilibrium that produces many combinations of option strategies and allows arbitrageurs to keep values in line. Additionally, the understanding of these basic building blocks is essential for a risk manager to understand the true risk of a portfolio of assets and positions. THE GREEKS The Greeks are the derivatives of the valuation equation that help measure the instantaneous risk of an options portfolio. Although these measurements offer risk managers and traders an excellent snapshot of risk at a given moment in time, a true professional is also aware of how these indicators will change with market movement and time. The most important ones for electricity options are delta, gamma, vega, and theta. Delta, known as the first derivative, is simply the rate of change of the value of the options portfolio with respect to the change in the underlying asset. Since the delta of a portfolio will change with movements in the market, an instantaneous delta measurement will sometimes be misleading. A trader and risk manager should know how his or her delta will change in time and space. A position with a positive delta will, ceteris paribus, increase in value when the underlying electricity forward price increases. The delta of a portfolio has a range between negative infinity and positive infinity. Gamma, known as the second derivative, is simply the change in the portfolio’s delta with respect to the change in the underlying asset. In other words, when a portfolio is long gamma, it will increase its delta value as the underlying electricity forward price increases and decrease its delta as the underlying electricity forward price decreases. The gamma of a portfolio will be dominated by the at-the-money options (long options, long gamma; short options, short gamma), and its absolute value will increase as time to expiration decreases. The gamma of a portfolio has a range between negative infinity and positive infinity.


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Vega is simply the rate of change of the value of the options portfolio with respect to the change in the implied volatility. Most traders and risk managers use “dollar vega,” which measures the change in the monetary value of the portfolio with a 1% move in volatility. A trader who is long vega will profit from implied volatility increases. The vega of a portfolio will be dominated by longer-term at-the-money options (long options, long vega; short options, short vega), and its absolute value will decrease as time to expiration decreases. Theta is the rate of change of the value of the options portfolio with respect to the change in time. Most traders and risk managers use “dollar theta,” which measures the change in the monetary value of the portfolio with a one-day change in time. A portfolio that is long theta will lose money as time passes, ceteris paribus. The theta of a portfolio will be dominated by shorter-term at-the-money options (long options, short theta; short options, long theta), and its absolute value will increase as time to expiration decreases. Theta and gamma have an inverse relationship in a particular portfolio. THE VALUATION OF OPTIONS Valuing electricity options is as much an art as it is a science as energy is an immature financial market. The science comes from such analytic solutions as the Black–Scholes formula, while the art comes from the trader or risk manager using such a formula to produce valuations which best resemble the future outcomes of the market. Traders and risk managers use models to help calculate the theoretical values of any products. The model’s main objective is to calculate the expected profit/loss and then discount it. The most commonly used models for electricity option pricing include the Black–Scholes model, the Black model, jump diffusion, binomial trees, and Monte Carlo simulations. Every model needs its inputs, and the standard inputs for commodity option pricing include underlying commodity (electricity hub, node, or zone), strike price, time to expiration, interest rate, and volatility. The underlying electricity forward price is the current price of the forward contract upon which the option is written. The value of calls will increase (conversely, the value of puts will decrease) as the electricity forward prices increase, ceteris paribus. The strike price is the exercise price or the price at which the particular electricity node will be physically delivered if exercised or financially

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settled against. The value of calls will increase (puts will decrease) as strike prices decrease, ceteris paribus. When the strike price of a call is lower than the underlying price, the option is in the money. The logic behind at-the-money and out-of-the money options follows. Interest rates are the current risk-free market interest rate. Interest rates are a less important input for valuing electricity options than for bond or stock options. With such high-volatility inputs and a low interest rate environment, the wide bid–offer spread present in electricity options make small changes in interest rates a minor issue. Time to expiration is the number of days before the option will expire. The value of calls and puts will decrease when time to expiration decreases, ceteris paribus. This concept is referred to as time decay. Volatility is the amount of fluctuation the particular electricity node, hub, or zone demonstrates over time, usually measured in an annualized form. There are two main volatilities that traders frequently mention: historical volatility and implied volatility. Historical volatility is calculated using historical data. Implied volatility is calculated by backing it out of an option pricing formula while using the current market price of the option and the other option inputs. Implied volatility represents the “market’s view” on overall option prices. This is the most important input as it requires the skill of the trader to forecast the future volatility to determine what an option should be worth. This is where the skill or art of the trader triumphs or fails. Volatility skew is one of the greatest challenges facing an option portfolio manager. Since energy price movements have “fatter tails” than the lognormal distribution of the Black model would predict, traders will price out-of-the-money options with different implied volatility than atthe-money options. This skew for forward electricity was quite flat when options began trading (out-of-the-money calls had the same implied volatility as at-the-money and out-of-the-money puts) in the mid-1990s. This phenomenon was the result of many utility hedgers dominating the market willing to sell calls against their generation and few experienced traders realizing the potential for explosive underlying price behavior. Subsequently, the volatility skews “blew out” in the late 1990s in favor of calls over puts. In recent years, due to the widespread “heat rate”1 trading and massive increase in natural gas-fired generation, the current volatility skew of forward electricity markets is similar to that of the natural gas option markets. Therefore, electricity calls will have a gradual positive call skew and a slightly negative put skew. This current situation presents an


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opportunity for current traders who understand the fundamental dynamics of their electricity price levels, particularly when natural gas-fired generation is not “on the margin” or when the electricity prices break their correlation with natural gas prices. Nonetheless, this skew continually changes over time due to seasonality and risk perception. In regard to risk management issues, volatility skew should definitely be used when pricing options. However, it is not clear whether using volatility skew in a “risk slide” is helpful in understanding risk. Valuing different strikes with different volatilities only complicates Greek analysis further. Often, risk models work best for the risk manager if they have fewer moving parts. In the extreme case of daily options, the skew is very pronounced on the call side due to occasional short-term supply shocks that occur due to the lack of storage capability. However, traders should not be too concerned with implied volatilities of short-dated options as the implied volatilities are based on probability theory that has little meaning in illiquid markets with short time frames. The option dollar premiums are a much more important concern as that premium will decay in a short amount of time. ELECTRICITY OPTION BASIC STRUCTURES With several different strike prices and maturities to choose from, option traders have thousands of strategies they can implement. In other words, there are thousands of ways to make and lose money in the option trading world, and energy options are no different. Strategies will mainly depend on the type (hedger, speculator, or arbitrageur) and risk tolerance of a particular trader. Many option strategies are just combinations of plain vanilla options that are quoted as option spreads. The option spreads most frequently quoted in the electricity markets include straddles, strangles, call spreads, put spreads, collars, and spread options. A straddle is the simultaneous purchases (or sale) of a call and a put with the same strike price. Straddles are used to bet on the change of volatility of the market. Additionally, straddles might be purchased to replicate owning an asset (synthetic asset) or sold to monetize assets already owned. Speculators might sell straddles if they feel the market will consolidate in a tight range so they can collect time decay premium. Market makers will use straddles to gauge implied volatility levels and reduce their volatility risks.

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A strangle is the simultaneous purchase (or sale) of an out-of-themoney call and an out-the-money put. It provides a lower-premium bet on future volatility of the market. A vertical spread (call spread or put spread) is the simultaneous purchase and sale of two calls (or two puts) with different strike prices and the same maturity. Buying the lower strike in a call spread allows the speculator to engage in a bullish position with low cost and limited upside. A collar is the simultaneous purchase of an out-of-the-money call and sale of an out-of-the-money put (or the reverse). Hedgers and producers often sell collars to protect against falling prices. A popular product for electricity hedgers is a costless collar, which means the call sold and the put bought have the same premium. Each of the general classifications above offers various uses in the mitigation and/or creation of risk for an entity. A company may alter the risk profile of its portfolio along many time dimensions through the use of the instruments above and also engineer more exact alteration through the combination of options and forward contracts as well as through the combination of various option types. COMMONLY OBSERVED OTC ELECTRICITY OPTION PRODUCTS While the markets for electric power options are by no means as robust as those for interest rates or currencies, a number of structures can be observed with some regularity. The majority of the electricity options in the past 10 years have been physically settled monthly and daily options. These contracts trade in 50 MW “on peak” (5 days per week × 16 hours) in the US Eastern Interconnect and 25 MW “heavy load” (6 days per week × 16 hours) in the US Western Interconnect, “around the clock” and, to a lesser extent, “nights” (7 days per week × 8 off peak hours). Additionally, financially settled options are becoming more popular all the time. These options are settled against an index such as the Dow Jones Daily Price Index or against the locational marginal price published on the PJM Web site (www.pjm.com (the Pennsylvania Jersey Maryland Interconnection in the US mid-Atlantic region)). These provide a much cleaner way to hedge and speculate upon risk, avoiding the hassles of daily exercises and scheduling. The future growth in electricity options business will be led by financial contracts such as these as they break down barriers to entry, such as infrastructure and credit needs.


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Valuation Note: Black’76, a model developed by Fischer Black for valuation of options on commodity forwards, is most commonly used among practitioners for valuation of monthly instruments. The primary motivation for its use is the model’s non-reliance on spot pricing, which proves very difficult to incorporate in the highly seasonal energy markets. Many commercially available versions of this model exist; however, for those who do not wish to use them, the models for put (p) and call (c) valuation are as follows:

c = e − rt ⎡⎣ f Φ(d1 ) − x Φ(d 2 ) ⎤⎦ p = e − rt ⎡⎣ x Φ(− d 2 ) − f Φ(− d1 ) ⎤⎦ , where

d1 =

log( f / x ) + (σ 2 /2 )t

σ t

d2 = d1 − σ t . Source: http://www.riskglossary.com/link/black_1976.htm

“Monthly” European options are the most common among these, which grant the holder, in exchange for premium which may be paid at transaction or at various agreed upon points throughout the term, the right to exercise his or her option to put or call a discrete monthly forward for some known set of hours. These instruments are often traded in seasons, such as “summer” (e.g. July and August), or as “calendar strips,” which comprise all 12 months of the year yet offer discrete expiry events. “Daily” European options, which grant the holder, in exchange for a premium which may be paid at transaction or at various agreed upon points throughout the term, the right to make a daily election throughout the period to put or call one discreet daily forward for some known set of hours, are also commonly observed in US power markets. These instruments also typically trade in seasons or calendar strips, as above. Daily “spark spread” or “heat rate” options are the most commonly traded “two-asset” options. This structure grants the holder the right to call or put electricity as a function of some fuel index and may feature financial settlement for one or both “legs” or may result in the actual delivery of each commodity. These instruments also typically trade in seasons or calendar strips, as above.

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Valuation Note: Numerous practitioners also employ Black’76 as a means of valuing daily European options; however, given that as many as 31 expiry events may occur within a month’s time, caution must be exercised in developing an appropriate time (T) function to represent the evolution of the underlying. Common practices include modeling each day as an individual option, choosing a “midpoint” for T, or simply adjusting the volatility to account for the greater premium value observed in dailies. For those not bound to the use of analytic solutions, Monte Carlo simulation offers a means of capturing the observable price process parameters as well as introducing one or more day over day correlation terms to account for the “memory” exhibited in many markets. Source: Kristufek and Oakes

Swaptions, or “one-time” options, grant the holder the right to put or call a collection of discreet forwards (e.g. a calendar swap) at some known rate on one specific date (prior to the expiry of the most prompt discrete contract) for the entire duration. Swaptions, by definition, trade for periods greater than one discrete month, yet the holder may make the exercise decision only once for the entire term.

Valuation Note: The most common means of spark or heat rate valuation is by means of a “spread option” model, many of which are commercially available. This is a classical representation featuring Black-like price evolution with a correlation term to relate the two underlying commodities. The value of “spread options” depends on the volatilities and the correlation of the underlying assets. A Black–Scholes variation model will take these inputs and create a three-dimensional probability distribution. Next, the model will take a 3-D slice of when the option is in the money and sum the probability-weighted payouts. Correlation has an inverse relationship and a large impact on the output price of a traditional spread option model. Therefore, it is essential for the trader or risk manager to have an accurate view on correlation in order to get an accurate valuation. As mentioned above, another alternative in valuing such instruments is Monte Carlo simulation, wherein more than the intercommodity correlation term may be accounted for. Further, since generation assets are also a kind of spread instrument (i.e. the right to convert some fuel into electricity at some known rate), the practice of Monte Carlo simulation may also be useful to the risk manager since the unique characteristics of a generation unit may be represented with greater accuracy than would be the case with the analytic solution. Source: Kristufek and Oakes


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Valuation Note: Swaptions, similar to the daily Europeans mentioned above, may be evaluated in the Black framework; however, the same caveats concerning time to expiry (and in this instance, time to maturity since they cannot be the same) also apply. Many practitioners in the energy as well as fixed income markets simply use an adjustment to the volatility input to represent the risk of a swaption. Alternatively, the use of a “basket” option model may be more appropriate in the absence of a highly visible and liquid market for swaptions since it allows for the recognition of intermonth relationships (i.e. correlation among the forwards that make up the swap). Numerous commercially available basket models are available, and the correlation terms required to populate them may be implied from either time-spread options, historical observations, or similar structures on highly correlated products. Source: Kristufek and Oakes

Traders and risk managers must understand the limitations of the models they do choose to use. An option model is a powerful tool which uses the laws of probability to give the trader an understanding of where the market volatility is. However, any trading model is only as good as its user. Some models are superior at valuing risk in the short run and others over a longer period. No model is perfect. The key for a trader or risk manager is not to search for the perfect model, but rather to understand the limitations and freedoms of the one they choose to implement. One obvious example of understanding limitations would be using the Black–Scholes model near expiration. Since option models are based on probability distributions, the smaller number of “trials” translates into diminished accuracy. Just as a statistician would not flip a coin only three times to determine the probability distribution of heads versus tails, a trader should not rely on the accuracy of an options model three days before expiration. POTENTIAL PORTFOLIO APPLICATIONS The managers of demand portfolios may have a great number of exposures embedded in their portfolios due to varied types of sales agreements (e.g. fixed rate, fuel related, or power index related) that are difficult to mitigate with standard linear instruments. Given the highly uncertain nature of demand portfolios, the use of options by managers may also be useful in creating volumetric (at stated prices) boundaries during times of unexpected demand (puts for disposal of excess and calls for accumulation of supply). Options may also be useful in altering the pricing relationships, and therefore the risk profile of a portfolio, to more closely match “natural,”

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existing, or simply the desired risk relationships—for example, the transformation of indexed supply agreements to match the obligations of fixed rate load obligations. The use of options is not limited strictly to the “lower-order” problems of demand portfolios; retailers may wish to acquire volatility products to insulate them from the effects of volumetric “swing” in highly volatile price environments or may wish to raise premium from otherwise highly predictable portfolios by “selling volatility.” Options also allow progressive retailers to meet client demand for lowercost products by purchasing embedded structures from the clients (e.g. “extendable” contracts, interruptible contracts, or bounded index products) that afford lower sales rates, yet preserve “value” for the provider. Many argue that the elements of most supply portfolios are themselves call options (“real options” or otherwise) as such use of options by managers of such portfolios is already well underway. However, as demonstrated above, the use of options in concert with each other and also linear instruments can yield markedly different portfolio characteristics. Since we have learned that all holders of options share certain exposures to such things as the level of underlying prices, volatility levels, degrees of correlation (both temporal and across commodities), the passage of time, and interest rates, it is logical to conclude that these same exposures exist within the supply “book,” and therefore a means of mitigating them must also exist. In fact, if correctly accounted for, managers may insulate supply portfolios from any or all of these effects through the use of complementary option positions that correctly offset the value response of the held elements. Given that electricity generation units and many supply agreements (e.g. tolling contracts) share several characteristics with commonly observed options, managers are free to transform them in similar ways too; in other words, the owner of a power plant need not be limited to the natural “long power, short fuel” position; he or she may strategically eliminate any exposure to those prices whatsoever and leave only the residual volatility and/or correlation position(s). In the extreme case, the holder of such a plant or agreement may completely transform the payoff characteristic by “converting the call” through the sale of linear power and purchase of linear fuel, thereby creating a put option for the holder with characteristics identical to the call. The careful reader will note that this need not be done for 100% of the notional volume; in fact, if done for 50%, the holder will have a call position (natural risk of the generator) equal to half the volume and a put position equal to half the volume with a strike price equal to that of the call—in trader’s parlance, a straddle.


Option Applications: Managers of demand portfolios are likely to serve a wide array of client types, ranging from residential to large commercial and industrial clients, many of whom demand innovative product offerings designed to meet the needs of the particular firm or industry and to maximize value. The strategic use of options can allow retailers to design such products to fit specific needs while also creating the opportunity to add value through differentiation; some basic examples of end-use products enhanced with the options discussed previously are detailed below. ● Interruptible supply agreements are among the oldest and most common uses of options in the electricity markets and pre-date deregulation by a considerable time. The rules governing such transactions vary widely, but in the interest of simplicity, a 16-hour interruption window will be considered here. Transactions of this nature, as the name implies, allow for interruption of an end user’s demand for some stated period (16 hours, or the peak period of one day in this example) in exchange for a rate below prevailing market. The reader will note that this structure represents the purchase of a daily European call by the supplier plus the supply of enduse energy. The end-use energy may be sold at a rate below prevailing market as the premium or value represented by the call will finance the difference in price at transaction. Upon consummating such an agreement, the supplier may chose to decompose the transaction into the forwards and daily options or simply position all or part of the package. ● The buy through structure is markedly similar in nature to the interruptible package mentioned above; however, as the name suggests, the client will posses the right to purchase the interrupted supply at some known and higher rate during interruption events. Assume that the interruption parameters are identical to those in the previous example. The reader will note that like the interruptible package above, the supplier will have purchased a daily European call, sold the client’s required supply, but will also have sold a higher-strike daily European call back to the end user. This combination of instruments represents the purchase of a “vertical” call spread by the buyer, premium (which will always be smaller than the premium for the “outright” call purchase detailed above, ceteris paribus) for which is applied to finance the difference between the client’s discounted rate, and the prevailing market value of the electricity. Similarly to the previous structure, managers may choose to immediately decompose this package into its forwards and call spread to monetize it immediately, or it may be positioned in the portfolio. An extendible transaction commonly involves an agreement to supply energy for a stated term (term A) at a rate below prevailing market in exchange for the right (held by the supplier) to continue this agreement for some stated term (term B) in the future at a price agreed upon at transaction. The reader will note that this transaction represents the purchase of a put swaption by the supplier for term B contemporaneously with the agreement to supply during term A. The value or premium associated with the term B put swaption is applied to the sales price of energy during all or part of the term. At this point the transaction may be decomposed into its respective forwards and put swaption and monetized, or it may be positioned within the portfolio.

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CONCLUSIONS We hope that this brief explanation of the electricity options market, from the perspective of two experienced traders, will be fruitful in order to monitor, explain, and manage the risk stemming from all types of portfolios. We hope that the insights contained here will allow you and your firms to further the development of our nascent markets, and therefore our industry as a whole, with the introduction of novel and effective risk management products to efficiently and effectively transfer and mitigate the risks native to our business. NOTE 1. Heat rate is the efficiency rating of a generation unit.

C H A P T E R

18

The New Weather Risk Market Hedging and Trading Strategies Brian O’Hearne

WHY IS WEATHER IMPORTANT? Unpredictable weather conditions can inflict losses on many companies. To handle this risk, weather dealers offer tailor-made structured weather insurance and derivative solutions that protect company earnings against weather events. Weather derivatives have gained a substantial foothold in the last few years, not only as a hedging tool, but also from a trading perspective. The modern weather risk management industry began in 1997 and has since grown to a global business that transfers over $40 billion of notional value annually. Initially developed in and utilized by utilities to manage temperature-sensitive volumetric risk, an increasing number of industries are now realizing the potential impact on their businesses, including agricultural concerns such as grain storage, handling, and transportation, to construction, and to entertainment, such as hotels, theme parks, and resorts. Weather drives a significant amount of the price volatility in the increasingly interdependent energy complex. Supply and demand are key: changes in expected demand (e.g. seasons that are cooler or warmer than anticipated) and expected supply (e.g. hurricane threats, well freeze-offs) influence prices and price action of energy instruments, including natural gas, heating oil, coal, emissions, propane, and hydroelectric power. Similarly, weather drives prices in the agriculture sector from the supply side (heat, drought, and freeze). 269

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During the summer of 2005, fears of another Hurricane Ivan, the huge tropical storm of 2004, focused more attention on the upcoming hurricane season. The US 2005 tropical storm season saw 26 named storms. It was the first with 13 hurricanes and the first with three categoryfive hurricanes as well as the first with four major hurricanes hitting the USA. This weather was also coupled with an extremely hot 2005 summer and one of the coldest starts ever to the December 2005 heating season. These factors helped drive energy prices to record highs, followed by a sharp sell-off in natural gas once the feared cold winter of 2005 did not materialize in the USA. Similarly, but not as dramatically, price run-ups in the agriculture sector occurred with the record heat and drought in the corn and soybean belt in late June and July 2005. What Exactly Are Weather Derivatives? A weather derivative is a transaction through which payments from one party to another are made based on weather-related measurements. These measurements are typically provided by a national weather service or, in the case of Chicago Mercantile Exchange (CME) contracts, by MDA Federal/EarthSatellite Corporation. From a risk management perspective, weather derivatives ensure that corporations are in a position to meet investor expectations and deliver profits by meeting cash flows, even during times of bad weather. Volumes on the CME have exploded this year as more and more commodity traders see opportunities to pair weather contracts with their commodity positions as additional alpha generators or as natural hedges. The notional value of CME weather contracts has grown from $2.2 billion in calendar 2004 to over $36 billion through calendar year 2005. The customer base is still predominantly comprised of energy companies. But the growing market also includes new customers as diverse as golf courses concerned with too much rain, municipalities bracing for too much snow, and ski resorts or clothiers worried about too little snow. Weather contract premiums depend on a number of factors, including the future probability of certain weather occurring for a given period from a historical and actuarial perspective, future forecasts and trends, and the level at which the customer wants protection to begin (deductibles). Weather Risk for Various Players Weather affects the volume of energy that power companies can sell. It creates volatility in revenues and earnings. A mild winter reduces natural

CHAPTER 18 The New Weather Risk Market Hedging and Trading Strategies

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gas and electric power sales for heating, resulting in volume-related revenue shortfalls for utility and energy companies. Lower than normal rainfall reduces power production at hydropower plants, forcing utility companies to run higher-cost power plants. Excessive rain at planting or harvesting or hot and dry weather during the growing season adversely impacts agricultural concerns ranging from the farmer to the processor and downstream to railroads, barges, and elevators. By creating weather indices specifically tailored to client needs, weather derivative dealers provide two types of customized solutions for managing weather risks: (1) hedges for pure volume risks based on weather triggers; and (2) hedges for volume and price risks based on a combination of weather and energy commodity price triggers. Parametric weather triggers can be defined on a broad range of weather data including temperature, precipitation (rain, snow), precipitation-dependent variables (river flow, water levels, etc.), and wind (speed and direction). Most weather structures in the market have Cooling or Heating Degree Days as underlying measures. The best dealers offer both degree day structures as well as tailored indices to reduce our clients’ basis risk between their actual revenue shortfall caused by the weather and their weather hedge by combining weather measures as well as blending weather and commodity price risk. DEVELOPING A WEATHER STRATEGY: HOW TO IDENTIFY AND QUANTIFY THE RISK Weather-Related Earnings Exposure Companies have profits/costs that are weather-dependent, and achieving predictability is nearly impossible due to weather’s chaotic and increasingly volatile nature. Despite its magnitude, there is little that can be done to eliminate weather risk. Climate is what you expect, but weather is always changing. Weather cannot be avoided, cannot be prevented from occurring, and cannot be segregated or isolated. Until recently, companies typically retained their weather risk or attempted to hedge with traditional financial or commodity products, often not meeting desired results. As weather and commodity prices have become more volatile, this is an increasingly risky proposition. Utilities frequently list weather as a reason for declining revenues: “New York, April 26, 2006 (Reuters)—Power company Exelon Corp. on Wednesday reported lowerthan-expected quarterly earnings on mild winter weather and higher operating expenses.” Construction companies also can have massive exposure, and blaming hedgeable risks is increasingly viewed negatively by stakeholders.

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Energy companies are very familiar with weather risk. Hydroelectric producers, for example, depend on both precipitation and temperature. If the snowpack is plentiful and summer temperatures are cool, not only may power prices be low but also the volume of sales will be low. Conversely, if precipitation is low and temperatures hot, the producer may be exposed to buying replacement power at high prices. Weather solutions can protect against both, and prudent companies are exploring weather-based precipitation as well as temperature covers. If there is low precipitation, payments can be structured into power prices. Agricultural markets are hugely impacted by weather. Low precipitation generally leads to low yields. This can impact not only the producers but also grain handlers such as elevators, railroads, barge transportation, processors, and a myriad of industries. Weather-based yield solutions, based off of temperature or combined temperature and precipitation, offer what can be very attractive pricing because of the dominant protection in the weather market being utilities exposed to cool summer (low sales) risk, where the agricultural concerns have the opposite, hot and dry exposure, which may balance a dealer’s portfolio. Combining price risk with the weather exposure through quanto products where the weather measure may trigger a payout in a commodity price creates even more flexible customer programs, where the best dealers can combine weather and commodity price protection. The use of weather derivatives has moved beyond energy companies. Golf courses sell umbrellas for a reason—customers need them if it rains. As golf course managers realize, storms not only cause golfers to stay indoors, but rain also means lost revenue in green fees, refreshments, and pro shop purchases. Similarly, prolonged drought leads to excessive irrigation costs and, if accompanied by excessive temperature, to decreased revenues. The summer of 2005 brought on severe weather conditions in the US, which caused many golf courses to incur large irrigation costs and run the risk of rapidly deteriorating fairways and greens as well as decreased play. Construction, entertainment (resorts, theme parks), retail, chemical, and even pharmaceutical companies (allergy medicine sales, for example) are impacted by weather. Weather Risk Solutions Volume-related solutions are based on cumulative weather indices over a specified calculation period (Table 18.1). The hedges can be structured as swaps, floors, caps, or collars, etc. The strike is the index value at which


T A B L E

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18.1

Volume example: floor If actual CDD falls above the strike, no payment is made Value Objective Strike Tick Period Limit Actual CDD Payout

Limit revenue shortfall 3,200 CDD $50,000 per CDD June 1–August 31 $10 million 3,100 [max(strike – actual CDD, 0)∗tick, limit] $5 million

Source: Swiss Re

payment occurs. The per-unit payment from the strike is specified by the tick amount. The payout is the positive differential between the index and the strike amount multiplied by the tick. The maximum payout is defined by the limit. Assume a utility company wants to hedge against a cool summer and chooses Cooling Degree Days (CDD) as its weather index for a put structure. The tick is set to the company’s incremental revenue per CDD, assuming temperature is the main variable driving power demand. The strike is chosen to reduce the company’s downside risk related to a drop in energy sales volume during a cool summer to a level commensurate with its risk appetite and the costs it’s willing to pay for the hedge. Volume and commodity price-related solutions (Table 18.2) are based on both cumulated weather indices and commodity price risks over a specified calculation period. Also known as quanto or quantity-adjusted options, these double-triggered hedges can be structured as swaps, floors, caps, or collars, where Payout = weather risk ∗ price risk. Quanto solutions allow for a variable payout per unit of the underlying weather index. For example, in the above CDD structure, the fixed tick amount is substituted with a variable power price index, reflecting the actual value of the drop in energy sales volume during a cool summer. The structure of this reduced basis risk solution can be tailored to the specific

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T A B L E

18.2

Volume and commodity price example: floor If aggregate rain falls above the weather strike or power price falls below the price strike, no payment is made Value Weather strike Price strike Notational Limit Agg rain Power price Payout

30 inch rain $50/MWh 1,800 MWh/inch $10 million 20 inch $80/MWh min(max(weather strike – agg rain, 0)∗notational ∗max(power price – price strike, 0), limit) $540,000

Source: Swiss Re

needs of a company. Table 18.3 shows a combination of weather indices in combination with various commodity price risks (electricity, natural gas, heating oil, and propane). The most common weather risk management products are based on temperature and include custom products such as over-the-counter swaps T A B L E

18.3

Offering of weather structures by Swiss Re Structure

Features

Products

Volume hedges based on weather triggers Combined volume and price hedges (quanto solutions) Out-of-the-money risk transfers At-the-money solutions based on capital market or insurance products Blended coverage (risk financing + risk transfer) Precipitation-based: rainfall, snowfall, river flow Temperature-based: HDD, CDD, critical days Average wind speed 3 months to 5 years Up to US $75 million per transaction

Structures

Indices

Tenors capacity Source: Swiss Re


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or options or exchange-traded products indexed on the degree day developed to relate the demand for energy to daily temperature and the assumption that if the average daily temperature is above 65°, there could be cooling demand and if below 65°, that heating demand may arise. The most common instruments in the weather market are temperature-based— heating degree day (HDD) and cooling degree day (CDD)—contracts.1 The reference point for countries that use centigrade is 18°C. There are also cumulative average temperature contracts which are, just as the name implies, based on daily or hourly averages over some period of time, such as a month or season. These are very popular in Japan as well as parts of Europe. The standard CME products are HDD or CDD options or futures. The CME offers contracts currently on 18 locations in the US, nine in Europe, and two in Japan. Dealers will offer contracts on almost any location where there is a good history of reliable weather data to minimize the basis risk to the customer. The best dealers will offer combinations of weather variables and can also bundle the commodity price protection into the offering, such as payout for hydroelectric producers that are referenced to precipitation but paid out in power prices. Weather dealers offer all the flexibility that traditional financial derivative contracts offer, including options, swaps, caps, floors, collars, strangles, fences, and the like, with payouts defined as a specified dollar sum multiplied by differences between the weather level or “strike” specified in the contract and the actual level which occurred during the contract period. Measures include: ●

●

●

● ● ●

● ●

Degree Days (HDDs, CDDs, customized such as growing degrees); temperature (Max, Min, Events); increasingly popular and great traction in Japan in particular; precipitation/streamflow (Inches, Events); hydroelectric concern, low precipitation; water and construction companies, concern high; wind; direction, windspeed coverage for outdoor events, fruit drop; weather-linked notes and bonds; combinations (custom index such as frost days, quanto, multiple trigger); load and yield products; electricity demand products (e.g. PJM demand swap);

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● ● ● ● ● ●

agricultural yield products (e.g. grain yield); contracts (swaps, calls, puts, collars, exotics, baskets, etc.); term (monthly, seasonal, multi-year, etc.); limits; most transactions capped to create finite exposures; hybrids (weather-linked financings, securitizations); various instrument forms (derivatives, insurance, reinsurance). Market Potential

The US Department of Commerce estimates that nearly one-third of the US economy, or $3.5 trillion, is impacted by the weather, with the possibility of that number growing due to the increasing interdependence of the weather and commodity markets. Assuming there is a 10% revenue impact on variations on weather, the potential US weather market becomes a $350 billion market. On a global scale, the weather market is dominated by the US and primarily from the energy side. Western Europe, Japan, and Australia are growing, and Taiwan, Korea, India, Latin America, South America, and South Africa are showing significant interest, particularly in non-standard indices and products such as precipitation, humidity, and critical day count structures. DEVELOPING A WEATHER STRATEGY Define the Risk or Opportunity The first step in evaluating a weather hedging or trading strategy is to define the risk or opportunity. What is the customer’s core business, and how is it impacted by weather? Does excessive or deficient rain, temperature, snowfall, sunshine, or humidity affect the company’s cost or revenue structure? Obtaining the relevant historical weather data is then relatively easy and affordable from most national weather services. Trends and other nuances of the weather data can then be explored and the weather data de-trended. Compile Historical Consolidated Sales or Cost Data Companies should be able to analyze their sales or costs data from their historical financial statements.


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Compare Detrended Sales Data with Detrended Weather Data Further, most companies that have weather sensitivities have an intuitive sense of how weather affects their costs or revenues—farmers know that heat and drought decrease yields, propane distributors that warm winters decrease sales or that snowy winters may not only decrease demand but impair deliverability. But what are the sensitivity points? Analyze the Relationship Between Historical Sales and Weather Data By doing a simple linear regression, the relevant cost or revenue consideration can be compared against the relevant weather data; Figure 18.1 shows the average daily peak load in ECAR (a power hub) versus weather. Calculate Weather Exposure for the Business Does the business have a single location or multiple points of concern? If multiple points, baskets of locations can be utilized or a regional weather index employed. For example, consider the following scenario.

F I G U R E

18.1

Temperature of a proxy for power demand

Source: Swiss Re

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NatGas, Inc., a local distribution company, services mainly residential customers. With sales concentrated in the northeastern United States, NatGas, Inc. is heavily affected by winter temperatures. The number of fuel oil gallons sold is highly correlated with Heating Degree Days, with sales distributed as follows: New York, NY, 57.20%; Boston, MA, 24.50%; Philadelphia, PA, 12.00%; and Washington, DC, 6.30%. Internal analysis determines that the marginal impact of one MMBtu on NatGas, Inc.’s profit is $.05. Regression analysis indicates that on average, one less HDD decreases natural gas sales in the region by 200,000 MMBtus in the period November–March. Therefore the impact to NatGas, Inc. is $10,000/HDD. NatGas, Inc. will purchase a weather risk management program to mitigate lost sales due to mild (warmer than normal) winter temperatures. Structure: Heating Degree Day (“HDD”) Floor (“Put”) Index: Cumulative HDDs Term: November 1, 2005–March 31, 2006 Stations: New York, LaGuardia, 57.20%; Boston, MA, 24.5%; Philadelphia, PA, 12.00%; Washington, DC, 6.30% Floor Strike: 3,130 HDDs Payout: $10,000/HDD Limit: $10,000,000 Premium: TBD (to be determined) Define the Risk to Be Transferred If the risk is a single, weather-dependent risk such as temperature, the strategies shown in Figure 18.2 could be employed. Or the exposure could be a combination, such as for a utility in the summer that is concerned with both temperature and humidity for determining its sales and system load requirements. The best dealers can combine various weather measures and can even combine weather with commodity price risk for a custom product that mitigates much of the basis risk from a pure weather hedge (see Figure 18.3). In general, a typical utility will have an exposure proportional to the product of volume and prices. These companies can use price derivatives to hedge the risk of prices and weather derivatives to hedge the risk of volumes (demand). The problem is that there is a portion of risk that is based on the simultaneous change of prices and weather. Neither of the respective markets individually (price or weather) captures this risk.


F I G U R E

18.2

Exposure plays

Source: Swiss Re

F I G U R E

18.3

Price and weather risk combined

Source: Swiss Re

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The knowns are: ● ● ● ● ●

normal weather: expected volume purchased at known price; colder weather: demand increases at “known” rate; warmer weather: demand falls off at “known” rate; very cold weather: may have to buy from market; very warm weather: may have to sell excess supply into market.

The risks are: ● ●

very cold weather and market prices impacting sales margins; very warm weather and market prices below purchase price.

An effective risk management product is one that pays when the company: ●

●

expects to (1) purchase commodity in the market and (2) prices are above $X/MMBtu; expects to (1) sell commodity in the market and (2) prices are above purchase price.

Select the Hedge Like other financial derivatives or insurance solutions, the type of protection can be quite custom, as illustrated above, or can range to a very standard product, such as a future or option on the Chicago Mercantile Exchange. Define the Risk to Be Transferred via Weather Insurance or Derivative How much of the risk is the company willing to retain? This entails a cost/benefit analysis. Obviously, the closer to normal that protection is desired, the more expensive the hedge becomes. Evaluate the Cost /Benefit of Each Alternative At this point, the company will have performed quite a bit of work on analyzing its exposure and examining different structures and alternatives and will have a good understanding of the various forms of coverage. The company then will need to do a cost/benefit analysis. Some companies that are more familiar with weather risk products have begun to manage their exposure at the mean; any deviations that are weather-based are viewed as negative by their investors and analysts. An example of this type of company is a utility that may be on a negative credit watch and that is penalized for negative earnings and not necessarily rewarded for


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weather-related upside. This company might consider a swap where they receive payment if weather is below average, say, in the summer, and they pay on the swap if weather is above average. Other companies are more interested in protecting against catastrophic risk, where an option would be more appropriate. Evaluate the Counterparty’s Credit Partially due to the newness of the weather market, very few dealers have substantial balance sheets. Clients should carefully examine the credit quality of the dealer and consider requiring a letter of credit for dealers with a credit rating below an A rating from one of the credit rating agencies. This is particularly important for any transaction longer than a month or two or for structured transactions that involve weather and commodity price risk. Trading Strategies Weather has a significant impact on price action in the commodity complex, particularly energy and agriculture. The increasing volatility and interdependence of weather and the commodity markets are creating new risks and opportunities. Weather impacts prices across the short, medium, and long term of the weather-sensitive commodity markets. Weatherlinked commodity trading is being paid increasing attention from banks and insurers as well as trading and risk management groups, such as hedge funds and energy firms. Weather drives price action in the energy markets from the demand (cooler or warmer heating/cooling seasons) and supply sides (hurricane damage in US producing region) and in the agricultural markets from the supply side. Commodity traders hedging or supplementing their positions with weather trading creates additional liquidity and transparency for weather markets with great depth in established commodity markets. Energy and agricultural markets have key weather sensitivities— heat, cold, drought, and storm tracks. Weather contracts can be used as hedges of commodity positions or outright trading tools for those familiar with how the interdependence of these markets operate. CONCLUSIONS Weather risk affects most industries and significantly impacts companies’ profitability. As a result, weather contracts serve a vital role in hedging risk across many industries. From a portfolio perspective, weather contracts

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offer one of the best non-correlated return opportunities relative to the traditional debt and equity markets. Weather contracts can also be additive return sources in conjunction with energy and agricultural trading opportunities, where new funds are integrating weather and commodity trading. Weather, like natural gas or corn risk, can be managed via an increasingly robust market. The interdependence and increased liquidity of weather markets creates new trading opportunities in both weather and commodity markets, where the potential structures are virtually limitless for dealers with creativity and the capability to combine weather and commodity prices into integrated offerings. Furthermore, clients are beginning to realize the benefits of hedging on a portfolio basis, where they can cover long-term exposures, mid-term, and even actively manage their businesses on a short-term basis based on changing weather forecasts. All of these result in further growth in the market and better liquidity and pricing for companies hedging their weather exposure. FURTHER INFORMATION Swiss Re: www.swissre.com Weather Risk Management Association: www.wrma.org Chicago Mercantile Exchange: www.cme.com

NOTE 1. An HDD is defined as the maximum of zero and the difference between 65ºF and the average daily temperature. The average daily temperature is computed by adding the maximum and minimum daily temperatures and dividing by 2. For example, a day in which the maximum and minimum temperatures were 40ºF and 20ºF, respectively, would result in 35 HDDs for that day: (65 − ((40 + 20)/2)). A CDD is defined as the maximum of zero and the difference between the daily average temperature and 65ºF. A day in which the maximum and minimum temperatures were 85º and 65ºF, respectively, would result in 10 CDDs for that day: ((85 + 65)/2) − 65.

C H A P T E R

19

Outlook for Asian Energy Markets Peter C. Fusaro and Tom James

INTRODUCTION The Asia-Pacific region is recognized as the major growth area for energy demand due to both demographics and the creation of new wealth opportunities. Higher energy prices, coupled with deregulation and globalization of markets, have added more price risk than ever before. The region is experiencing rapid economic growth, fueling increased needs for crude oil and refined products supply. It is the second largest oil-importing region of the world, after the United States. The single largest consumer in the region, China, is currently the world’s second largest oil consumer, behind the United States. This is having a dramatic impact on energy markets. Japan, India, and South Korea are also heavy fossil fuel importers. Growth in the region, and in China specifically, is leading to the development of new supply markets in both the Middle East and Russia. It is also leading to new infrastructure construction from point of supply to the refinery and beyond. This growth in demand is driving rapidly increasing supply chain complexity as new trading patterns develop. Since most shipments are undertaken by water, the new infrastructure includes tankers, terminals, storage facilities, refineries, and overland distribution systems. Similarly, changes in world natural gas markets are still driven by the huge liquefied natural gas (LNG) consumption of Japan, South Korea, and Taiwan and the rise of China and India as LNG consumers. 283

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World gas trade is still dominated by Asian demand and will be for many years to come. Coincidentally, Asian producers such as Malaysia, Indonesia, Brunei, Australia, and the Middle East dominate suppliers of LNG as well. ASIA’S RISK PROFILE In Asia, risk avoidance rather than risk management has been the operative phrase in Asian energy markets. However, this is changing as the twin engines of deregulation and privatization are driving competition in Asian economies. The business-as-usual approach no longer works. The region’s sharp increase in oil and gas dependency has added more price and supply uncertainty, consequently leading to more price volatility. Oil prices at $75 per barrel, plus high rice volatility, will force Asian energy consumers to use more risk management techniques to manage their price risks. The annualized price volatility of oil, which now ranges from 40% to 50% per year, is the highest of any commodity and is deeply impacting Asian economies. But more importantly, it is bringing added consideration to more active price risk management, rather than the preoccupation over the oil security of supply issue as in the past. Moreover, globalization of energy markets brings with it the need to actively manage price risk. In fact, the markets are becoming more price-sensitive with the rapid dissemination of price and market information 24 hours a day, seven days a week. The wise use of financial risk management tools is needed now more than ever. The energy markets are changing in many ways, both physical and financial. Trying to trade these markets with old playbooks does not work anymore since structural changes have taken place in energy trading. Nowhere is this change more apparent than in Asia. DIFFERENT MARKET EVOLUTION Although market developments have not followed the energy market evolution of New York and London, Asia is ripe for fundamental change in its trading and risk profile. While trading remains largely based on term, over-the-counter (OTC) contracts without a standard regional marker for price transparency, it is this supply chain complexity that will drive costs and risk in the medium term, both in China and the Asia-Pacific region generally. The risks and costs involved are becoming too great to rely on the in-house developed or spreadsheet-based energy trading, transaction, and risk management systems in use today by many of the region’s major energy companies.

CHAPTER 19 Outlook for Asian Energy Markets

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THE MARKET DRIVERS OF ENERGY TRADING Energy trading and the use of energy risk management tools have been slow to evolve in Asian energy trading since concerns about security of supply still dominate thinking. The rapid movement of China and, subsequently, India to secure long-term future supply from many global sources in the Middle East, Africa, Latin America, and Russia attests to that strategy. The present state of affairs is beginning to change across the board in the energy complex. As many Asian countries move toward open markets, competitive forces are coalescing that will force much more active energy risk management. It can be argued that risk is endemic in market economies. Therefore, risk management techniques will become the necessary survival skills of Asian corporations. Active energy risk management then becomes a fiduciary responsibility of Asian energy companies. While short-term physical oil trading has always existed in most Asian countries, the energy complex is now broadening to include gas, power, petrochemical, coal, and weather risk management. Lurking on the horizon is emissions trading to reduce plant emissions and reduce greenhouse gas emissions. Asia is now primed to embrace the active use of energy derivatives and much more sophisticated trading techniques and financial engineering. Borrowing heavily from the institutional memory of welldeveloped New York and London capital markets, energy trading and risk management are on an upward trajectory in Asia fueled by growing oil and gas dependencies and the need for more electric power. Credit risk management, similarly, is an area of exponential growth in Asia as the need to actively manage counterparty risk increases. MARKET DEVELOPMENT Energy markets are the most volatile commodity markets in the world due to dramatic changes in the physical markets, which are in turn influenced by unpredictable weather patterns, political events, and dramatic swings in supply-demand balances. These factors lead to tremendous price swings in short time periods. Thus the energy commodities—oil, gas, and electricity—are quite conducive to the use of price risk management tools. However, the innate conservatism of the energy industry has demonstrated a reluctant and slow acceptance of the risk management tools of the world financial markets. This process of market acceptance is now underscored by the deliberate and yet incremental development of energy derivatives in the Asia-Pacific region. The financial revolution that overtook Atlantic

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basin oil markets during the 1980s is still just getting started in Asian markets, even though Asia-Pacific oil prices continue to be inordinately influenced by Atlantic basin-oriented financial instruments for energy— Brent and WT1. While futures trading in energy began in 1978 with the New York Mercantile Exchange (NYMEX) launch of heating oil futures, it has taken longer than expected for the energy derivatives market to develop in the Asia-Pacific region. And the Asia-Pacific energy derivatives markets are following a different path from the more mature markets of London and New York. With no viable energy futures contracts, the OTC energy derivatives markets are leapfrogging energy futures market developments in the Pacific Rim region, with many OTC derivatives agreements created to meet growing market needs. And while the OTC markets can sometimes evolve into futures contracts, as 15-day Brent did in Europe, the OTC swaps and options markets tend to function like a quasi-futures market in Asia. These markets are unregulated, global, and both short and long term and are influencing prices beyond their notional value; that is to say, they are influencing price formation in the physical markets. The change in the energy commodity markets is being brought about by underlying developments in the physical markets in the Asia-Pacific region, including new and planned refinery projects, growing petrochemical capacity, rising electric power needs, development of a natural gas infrastructure (particularly for LNG), and a movement away from a high degree of government regulation of the energy sector in many countries. Capital flows to Asia in the coming decades will be substantial as greenfield energy projects proliferate. But this move toward deregulation will follow an Asian model and will not be a rapid transition to open markets, but a gradual process. In effect, a controlled deregulation process is under way. Moreover, Asia’s role continues to rise in importance in world energy markets so that risk management imperatives will be more pronounced in the coming years for it will be increasingly necessary for both oil producers and refiners to use these instruments. THE ASIA-PACIFIC REGION IN THE GLOBAL SUPPLY SCHEME During the present decade, continued economic growth for the Asia-Pacific countries will bring added pressure on regional Asian oil and gas markets from demand pull in global energy markets. The Asia-Pacific energy


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markets are heavily dependent on hydrocarbons, including roughly 50% coal, 30% oil, and 10% natural gas, with nuclear and hydro constituting the remaining 10% (on an oil-equivalent basis). Gas usage, except for LNG, is relatively new in the region and will require more investment in a pipeline infrastructure and distribution facilities, but it is sure to rise. This region accounts for roughly 25% of world oil demand, 45% of coal, and 10% of natural gas demand. The importance of the Asia-Pacific region in terms of world oil demand and refining cannot be overstated. Since 1985, Asia has accounted for over 70% of total world oil demand growth. This area has already surpassed Europe and will soon eclipse North America as the primary region of world oil demand. The Asia-Pacific continues to be the most dynamic oil market in the world, with demand predicted to increase to 29.4 million barrels per day (b/d) by 2010. Most of this increased consumption will be sourced from the Middle East, from where, presently, over 70% of the supply comes. It is estimated that 80% of Persian Gulf oil production will be exported to China, India, Japan, South Korea, Taiwan, and the Association of Southeast Asian Nations (ASEAN) countries by the year 2010. Growing commercial ties between the Gulf producers and Asian consumers seem inevitable, especially as the giant US market shifts to a greater regional dependence on Latin American producers and away from the Middle East. Japan, South Korea, China, India, Taiwan, Thailand, and Singapore are now all importing oil at over 1 million b/d each. While some Atlantic Basin crudes from West Africa and the North Sea may supply some of the older, less flexible Asian refineries that have an appetite for those sweet crudes, the key issue is the growing Asia-Pacific dependency on Middle Eastern sources of crude. This increased dependency on oil presages an era of continued price volatility and the growing need for more risk management instruments to be developed and utilized in the Asian markets. China already turned into a net oil importer in 1993, and its needs continue to grow. And Indonesia, an OPEC member and current oil exporter, seems set to be a potential importer of oil. With about half of world oil growth projected to continue to be in the Asia-Pacific region, rising product demand, and tightening fuel quality standards driven by rising environmental awareness, the need for managing energy price risk seems poised for explosive growth over the next several years. However, it has taken an inordinately long time to get started in the

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region compared to the North American and European experiences, particularly because of the more protectionist Asian economies. Asia will need more imported crudes in the coming years as output declines in Indonesia and oil production increases only slightly in China, and even though increased output is likely in Australia, Malaysia, Papua New Guinea, and Vietnam for the short term. Sour barrels will come from Mexico and the USA. Moreover, product import dependency is also rising at an astounding rate. Changing markets and oil trade patterns presage rising price volatility. While many derivatives players continue to eye China as their next market for growth, commodity exchanges will take time to develop there. Occasionally, opportunities for using risk management tools are quite evident. China remains a wild card in the Asian energy markets since its demographics can change supply and demand needs very significantly on its road to economic development and industrialization. Moreover, the longer-term derivatives markets should develop with the use of commodityindexed loans to oil, used to finance large projects in oil and gas exploration and production, refining, and electric power generation. Growing oil import dependency will lead to more price volatility and supply instability. While coal is the region’s most abundant energy resource and dominant primary fuel, it is oil and gas demand growth that will rise substantially, entailing new financial risks. There is the possibility of coal commoditization in the Asia-Pacific region as global coal trading emerges. OTHER FUNDAMENTAL CHANGES UNDER WAY IN ASIAN OIL MARKETS Petroleum storage requirements for refiners and traders are another area impacted by deregulation, but they are also a growing area for risk management. Many storage expansions have been announced throughout the region. Singapore, as an active regional transshipment center, has already undergone more storage capacity increases. Subic Bay in the Philippines is another strategic storage location. China, India, South Korea, and Thailand have all announced that large-scale storage projects are under way. These and other projects are an attempt to reduce the transshipment cost of Singapore facilities. The need for strategic stockpiling of oil and petroleum products will rise in importance for energy security reasons. It is a dominant part of the Asian energy puzzle. In fact, regional storage


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seems to be taking hold as both China and India have announced that they will build up strategic petroleum reserves. But the role of storage is changing as well. The use of strategic product storage for both oil security issues and to arbitrage physical market movements for petroleum products with paper instruments will become more pronounced. Changing fuel quality specifications requiring more blending for clean products as well as growing oil demand are influencing the need for more storage. Oil traders already see this opportunity and have acquired product storage. Paper structures are offered by these storage providers as well. Product storage arbitrage should grow in the coming years. CHANGES IN OIL SUPPLIERS Another source of growing energy supply to the Asia-Pacific is the emerging Russian Far East. The resource base of the Russian Far East is primarily the area of Sakhalin Island, including the offshore areas of the Sea of Okhotsk and the Yakutia producing area of Russian Siberia. Discoveries of oil and gas are also expected on the shelf of the Bering Sea. These areas will supply some oil, but mostly natural gas. This new supply of the Russian Far East oil and gas program is beginning to ramp up today. The Asian markets are also net importers of middle distillates. China and India are major importers from Europe as well as the Middle East. There can be active arbitrage of middle distillates from the European and US West Coast markets when opportunities arise. TANKER MARKET DEVELOPMENTS Tanker trade will also be significantly impacted by rising demand for imports of crude oil and petroleum products from outside the Asia-Pacific region. This factor will shift the use of vessels from the Arab Gulf to the West and the Far East to meet rising needs for very large crude carriers (VLCCs). Because incremental demand for refined products in the Asia-Pacific region will not be satisfied by the planned expansion of both Asian and Middle Eastern refineries, product carriers from more distant refineries, such as the Mediterranean and US West Coast, will be needed. The average tanker voyage will become longer. Smaller tankers will benefit to a lesser degree, and VLCCs may bring shipments from northwest Europe to Asia if product demand becomes too tight in the Asian markets.

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Obviously, this change will bring a rise in world-scale time charter rates and perhaps a rise in hedging activity for tanker rates, another nascent paper market for trading. Tanker markets have seen the emergence of their first hedge fund, with ship broker Clarksons in London launching a fund in 2005. We expect more freight rate hedging in Asia since it is such an active shipping market. ASIAN MARKET CHARACTERISTICS The Asian markets are evolving quite differently on the paper side from those in the USA or Europe. For the Asian paper markets, it seems that many smaller markets for both crude oil and petroleum products will develop rather than one crude oil marker, such as Brent or WT1, or singular benchmark petroleum products. At the present time, Malaysian Tapis, Oman and Dubai are OTC price markers for crude oil, but other paper markets for crude oils should emerge for various Indonesian, Australian, Chinese, Vietnamese, and Alaskan crudes. And proposals have been made to develop a basket of crude oils as a viable forward or futures market contract. This will probably be the best solution to the long-standing problem of how to hedge the Asian barrel and was proposed at an Asia-Pacific Petroleum Conference meeting in 1989. The petroleum products markets are already following the path of regional paper market development, with active markets for open spec naphtha in northeast Asia and markets for motor gasoline, jet fuel, gas oil, and high-sulfur and low-sulfur fuel oils. In fact, the development of a paper market for LSWR, the low-sulfur Indonesian fuel oil that is mostly used in the Japanese electric power market, is indicative of this change and trades one to three months forward. There are a wide variety of petroleum product swaps markets in Europe but only one successful energy product futures contract, which is the gas oil contract on the International Petroleum Exchange. One continually active paper market is jet fuel. Far Eastern airlines such as Cathay Pacific, Singapore Airlines, and Malaysia Airlines already use derivatives to hedge their jet fuel exposure, despite the China Airlines trading fiasco. These deals vary from one quarter to 18 months forward. The jet fuel derivatives market is influencing forward prices for jet fuel in the physical markets and has been active and well established for several years. Part of the reason for this evolutionary process in Asia is that OTC contracts are more flexible than futures contracts and can be developed more quickly. They are also not subject to regulatory approvals that are


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required for exchange-traded futures contracts. At present, the Asian markets also are more interested in the “plain vanilla” swaps than in more exotic instruments and structures. Another significant difference in the evolution of Asian paper markets is that unlike longer-dated instruments in Europe and North America, much of the business is shorter term (less than one year), although some three-year deals are written, mostly for gas oil. The role of swaps brokers in the Singapore market has also added liquidity to the paper markets in Asia. Unlike the swaps brokers in other oil trading centers, most Singaporean swaps brokers still trade both the physical and paper barrels because of the immaturity of the paper markets. The paper trade is less developed for swaps brokers but is evolving as more paper is being incorporated into physical deals, particularly for high-sulfur fuel oil. The Asian markets still tend to be more orientated to physical trading than financial hedging. CHALLENGES TO CHANGE One of the biggest issues facing the development of the energy derivatives markets involves creditworthiness, which can hamper the development of longer-term deals. The financial collapse of one major player can affect the market significantly. Therefore counterparty risk must be closely monitored and the financial strength of companies assessed as a matter of routine. This is not an insurmountable obstacle but will take time to overcome. Also, the proliferation of state-owned oil companies and state-owned utilities inhibits competition at the present time. However, the status quo is changing. The privatization and deregulation of these energy markets that will be coming in the next few years should hasten the development of more paper trading in Asia. At present, many of the paper market makers are said to be chasing the same business, but this is true of many immature markets, such as the electricity trading business in the US. While the Asia-Pacific oil trade is still centered on security of supply rather than price risk management, the Asia-Pacific derivatives markets are just beginning to emerge as the next opportunity for growth for the derivatives markets in energy. Fed by growing oil demand in the region and the growing interest in making Singapore the energy derivatives center for Asia, a change to a more financial rather than physical orientation in energy trading seems to be beginning. And political changes in the Asian countries as they move to deregulation should bring more active trade in both futures and derivatives.

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Currently, however, the OTC oil derivatives markets are providing the paper markets with liquidity and price discovery without viable Asian oil futures contracts. It is a similar development to the electricity derivatives markets in the US, which now goes to three and five years out or longer without an active cash market, viable futures contracts, or a price discovery mechanism. The highly publicized financial debacles in recent years, such as Enron and WorldCom, have focused attention on risk management. Consequently, there is now more interest in hedging and the use of energy risk management tools. What these financial disasters have done is to raise hard questions about the derivatives and futures markets and the need for companies to hedge. Senior management is now asking the right questions, such as: What is the natural hedge position of the company? Is the company long or short in the market? Should we hedge? These essential questions go to the heart of why energy companies must use price protection programs, for risk is much more complex than management of price risk only, and risk monitoring is now becoming a corporate fiduciary responsibility of senior energy executives. Energy hedging is still just getting started, with small amounts of oil hedged. Typically, commodities can trade daily volumes on futures exchanges at a factor of 6–20 times the physical market. The Pacific Rim, with its tremendous need for oil, gas, and power, is just beginning to develop the financial trading structure needed to manage energy price risk. It will be a growing part of the Asian energy complex. New financial risks need to be intelligently managed. Consequently, risk management, once considered a peripheral concept, has now become a key management tool. In fact, effective risk management can be an essential tool in achieving industry leadership. Having this capability allows growth opportunities by enhancing company competitiveness and supporting financial objectives. Risk management, when employed effectively, reduces market risks, increases wholesale and retail competitiveness, protects and enhances margins, and stabilizes earnings. The relative sophistication of an energy company’s risk management framework depends upon the nature and extent of the risk as well as the complexity of its transactions. In the Asia-Pacific markets, there is actually now less uncertainty than previously on the regulatory side since countries are making their deregulation plans quite transparent. Market credit and operational risks are still pervasive in the Asian markets. Components of market risk


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include price, basis, spread, liquidity, and volatility risk. Components of credit risk include counterparty creditworthiness, transaction attributes, and market conditions. Most importantly, a company’s risk tolerance must be identified, particularly since oil, gas, and power are the most volatile commodities traded. The objective of using risk management tools is simply to achieve corporate goals, and these are unique to each company. There is no cookie-cutter approach of one size fits all. These goals can include lower fuel costs, securing market share, reducing earnings volatility, or increasing margins. The key is risk reduction, not risk elimination (since that is impossible). While Asian energy companies have been slow to adopt these financial tools, the tools are more finely developed and the knowledge base wider than when they were accepted in the US and Europe in the early 1990s. Thus the Asian markets may have some advantages in using more sophisticated risk management software and having a more developed control structure so that trading does not go awry. CONCLUSIONS What is most important in the Asia-Pacific region is the security of oil and gas supplies. This continues to dominate Asian risk management strategies, which still focus on short-term trading and hedging. This is, in effect, a supply-balancing system. However, rising oil demand in the region, coupled with increased price volatility and followed by the rise of a global LNG market, will begin to change that type of thinking as hedging strategies become more sophisticated and management adopts a longer-term orientation. The integration of both physical and financial trading, which is more advanced in Western Europe and North America, will begin to influence supply logistics systems implementation and energy risk management. The Asia-Pacific derivatives markets, as markets, are evolving in a different manner from London and New York, with a proliferation of many different OTC financial instruments for many different products and crude oils. Derivatives will become more established in the Asia-Pacific region over time as oil demand continues to increase and more competition enters the oil markets in this region. Newer use of energy derivatives will include their use in project finance through forward oil and gas sales, commodity-indexed loans, and energy securitization. Energy hedge funds are just starting to appear in the region.


C H A P T E R

20

Green Trading: Environmental Financial Markets and Energy Trading Peter C. Fusaro

T

his book has focused on energy trading so far. In this and the following chapters we focus on the emerging environmental financial markets. INTRODUCTION The energy industry today is the number one emissions polluter on earth but will become the number one provider of environmental solutions. This is because it is a good business practice. The industry is at a tipping point on global warming as carbon intensity continues to grow. The world has a short period of time available in which to act to stabilize carbon dioxide (CO2) and other greenhouse gas (GHG) emissions. This issue goes far beyond the Kyoto Treaty, which ends in 2012. The USA accounts for 25% of GHG emissions and will never be a party to this treaty. Neither are developing countries to a large extent, and they are starting to focus on increased coal-fired capacity to meet growing electric power needs. Rather than point fingers at all these controversial issues, it is more important to focus on what can be done and how it can be accomplished. Trading and financial markets offer one opportunity to stabilize and fix a growing global environment problem. We need to focus on the period beyond 2012 for any real climate change mitigation trading scheme to take hold. That is under current negotiation. 295

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The energy industry has the financial strength, intellectual capital, and global presence to provide these solutions now. BP and Shell have already taken the lead, but many other energy companies are not far behind. The fact is that the carbon footprint of the major oil companies resonates globally with oil and gas production, refining, and transportation, and they are gradually making their presence felt in the power industry. The solutions will include the use of more efficient, environmentally benign technology as well as changing standard industry practices. The environment has now become a corporate financial issue. Greater financial disclosure of corporate environmental risks, including climate change, has raised the issue to a corporate fiduciary responsibility level. Environmental risk management must be proactively managed by corporations. In order to manage these new financial risks, a variety of risk mitigation techniques will be needed, including trading, project development, renewed investment in energy efficiency, renewable energy as a portfolio option, and a fundamental change in corporate business practices, to give added weight to environmental liabilities and considerations. The environmental performance and financial performance of companies are increasingly intertwined. This directly impacts automobile manufacturers, electric and gas utilities, oil and gas companies, banks and insurance companies, and all concerned about climate risks. Automakers are concerned about CO2 emissions per vehicle produced and sold. Electric utilities are paying more attention to reducing their GHG emissions footprint as part of their air emissions reductions. Oil and gas companies are increasingly concerned about emissions as production, refining, transportation, and distribution liabilities and are active in many locations. Bank share valuation could fall if they do not have adequate carbon risk management strategies. And insurance and reinsurance companies are now at the forefront of confronting these financial risks, such as catastrophic risk for crop failure due to climate change and health-related risks due to the linkage of climate change and infectious disease. These new financial risks for insurance and reinsurance companies may lead to their dropping coverage for certain companies, which they are beginning to do in the aftermath of Hurricane Katrina and other coastal storms in the USA (catastrophic risk bonds are one way to play this market). These new financial risks and liabilities have now become the market drivers for change and financial market creation. This corporate financial issue has risen at annual shareholder meetings at energy companies such as ExxonMobil, ChevronTexaco, and Southern Company, to name but a few. Innovest, the Green Moodys, has

CHAPTER 20

Green Trading: Environmental Financial Markets

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already shown that companies that are greener are also more financially successful. Thus companies have begun to analyze their financial risks and realize that there is a global issue that must be dealt with. While the good deeds of first movers such as BP, Shell, DuPont, Trans-Alta, and AEP are important, there is now a second wave of corporate engagement on the issue. Projects and trades have already begun. A lot of institutional money has flowed into project-based carbon emissions reductions. Green trading is now under way globally. The inflexion point was the EU Emissions Trading Scheme (ETS), which began on January 1, 2005, and the implementation of the Kyoto Treaty on February 16, 2005, which has catalyzed environmental financial markets. MARKET DEVELOPMENTS NOW UNDER WAY Energy trading began in 1978 with the first oil futures contract on the NYMEX. During the 1980s and 1990s, both the IPE (now called ICE Futures) and NYMEX successfully launched futures contracts for oil and gas. Today, these successful energy futures exchanges are the survivors of the Enron and merchant energy trading debacles. Oil companies, investment banks, and, increasingly, hedge funds now provide the necessary trading liquidity through market making on both the established government-regulated futures exchanges and off-exchange energy derivatives markets which can clear on the futures exchanges. Companies know how to manage their financial energy risk. They have the risk management skills that will increasingly be deployed in the emerging global environmental markets. Financial risk will be managed on established energy futures exchanges as the Enron debacle in particular has taught that financial performance in energy markets is key. Using that financial template, the environmental financial products for sulfur dioxide (SO2) and nitrous oxides (NOx) have proven to be successful solutions for pollution control in the US since 1995 and 1999, respectively. While a $20 billion environmental market today pales in comparison to a $2.2 trillion energy derivatives market, the growth trajectory shows that one must look at the green trading markets of today as the oil markets of 1978—except that this time the maturation process will be global and simultaneous as carbon trading regimes take root in the EU, Asia, Australia, Latin America, and North America. In 2005, according to Point Carbon, the EU traded E9 billion of CO2 emissions. The estimates are that a $3 trillion commodity market may emerge over the next 20 years. The dollar value of this market is enticing, but the reality is that the global

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energy industry will be the primary supplier of liquidity to this market, followed by the agricultural industry. Both industries are active in commodity trading. They are also active in carbon market development as they are impacted by growing environmental financial liabilities and risks. Green trading encompasses the convergence of the capital markets and the environment and includes not only GHG emissions trading but also both renewable energy trading and trading the financial value of energy efficiency. Cross-commodity arbitrage opportunities are self-evident as oil, gas, coal, and power have environmental dimensions, as do weather derivatives. Today, cross-border trades of CO2 have been conducted between many countries. Through the Kyoto Clean Development Mechanism (CDM) and Joint Implementation (JI), developing countries will be fully engaged in this financial market. They will be able to sell GHG credits and allowances through these Kyoto-based mechanisms as well as the need to provide technology transfer mechanisms through green trading. Green trading provides a market-driven solution to reduce pollution, but it needs mandatory government sanctions to get the rules in place. The US SO2 program is a “cap-and-trade” program that has a 35-year time horizon with the retirement of pollution credits from 1995 though 2030 (it will be described in greater detail in Chapter 21). A GHG regime will require a time horizon of 50–100 years to be effective and achieve carbon stabilization and effective GHG mitigation. That needs to be put into place now, not 15 years from now. The rules of government are also needed to deal with the cross-border components of trading. The trading rules need to be harmonized and easily verified. Most importantly, liquidity providers will include energy companies, banks, agricultural producers, the insurance and reinsurance industry, hedge funds, and investment banks. This emerging market took center stage in 2005 when both the EU ETS and Kyoto Treaty came into being. There is also a significant movement at the state level to form a cap-and-trade market in the US northeast. This is the Regional Greenhouse Gas Initiative, which is a collaboration of eight northeastern states. There is also agreement to work with the California Climate Action Registry to have conforming standards between the states. The fact is that the rules will now be in place to begin GHG trading in the US sooner than many imagine and despite the negative position of the Bush Administration on GHG reductions. Moreover, it also seems likely that the US federal government will get on board after the next presidential election in 2008. The US energy industry and others cannot have dual environmental standards in disharmony with

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the rest of world. This is rising as a friction point in the USA as well as internationally. Carbon market development will follow and continue to accelerate as 12,000 facilities in the EU must comply with the EU ETS. This creation of carbon credits will have multi-year streams. The hedge funds entered carbon trading in both the US and EU during 2005, and more are entering in 2006. They are looking for arbitrage opportunities in global carbon markets. While there are a large volume of structured trades, there is also spot trading on many European exchanges, one US exchange (the Chicago Climate Exchange), and on the over-the-counter (OTC) markets. There is speculative trading and some risk hedging. Fundamentally, we are witnessing a market transformation in energy companies and energy end users from the environmental department to the risk manager as some major corporations are handling the GHG issue as a financial issue. Carbon finance is playing a bigger role. It is the early stages of the market. It is a tremendous education process. The global market opportunity is huge, with a carbon footprint of 25 billion tons and growing. In 2005, the EU traded 362 million tonnes of CO2. Commodity markets typically traded 6 to 20 times the physical market. This means that a $3 trillion market is not out of the question. It is estimated that it will be worth $100 billion by 2010. WHERE IS GREEN TRADING TODAY? Green trading today is at the tipping point. Higher energy prices are pushing forward more renewables and clean technology. More stringent environmental standards throughout the world are also accelerating green trading market development. The existing green trading market can be characterized as having the following characteristics: opaque prices, little trading, few participants, poor liquidity, tremendous inefficiency, and wide arbitrage opportunities. If these attributes sound familiar, they are the primary factors of each emerging market. Having seen the emergence and maturation of oil, gas, power, weather, and coal as fungible commodity trading markets during the past 25 years, the environment is now well positioned to be the next financial commodity trading market. Uniquely, it will explode simultaneously throughout the world. Similar to oil market developments circa 1978, we are now seeing the emergence of global CO2 as a fungible commodity trading market.

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The green trading markets are intimately linked to energy. They are not only influenced by fuel type and usage, but also increasingly correlated to renewable energy usage and energy efficiency gains. It will create new project development in both the renewable energy and energy efficiency sectors, which will trade their environmental attributes. The global dimension of this market cannot be understated since we have not had a true global commodity form since the oil markets. As these other markets mature, we will see the triple convergence of environmental financial markets (see Figure 20.1, which shows the interrelationship of GHG and emissions reductions with renewables and energy efficiency). While the private sector will take the lead on the development of emissions trading markets wherever it has a vested commercial interest in emissions reductions, compliance responsibility will rest with governments. There are strong beliefs that markets will form first, thus creating an emissions trading marketplace, and that governments should not inhibit such growth. This new marketplace would motivate firms with surplus emissions rights to trade or supply those rights to the market. In effect, despite the risk of uncertainty on future rules, there are merits to moving forward early. But the need for regulatory certainty over the long term is paramount. It seems evident that industry-driven schemes will be grandfathered in the future as rules are more clearly defined. Thus industry can create its own domestic and international portfolio of emissions allowances or credits. Another emerging trend that may hold the key to GHG emissions liquidity is the structured finance market—“green finance.” A fuel type shift to greener and cleaner fuels such as natural gas, in preference to coal F I G U R E

20.1

Triple convergence in environmental financial markets

Source: Global Change Associates Inc. (www.global-change.com)

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or oil, is becoming embedded in the fabric of new power station project financing. Since these plants have a useful life of 20–40 years, they will bring a stream of emissions credits that can be banked or used up front. They are unlocking another avenue for market evolution. This type of thinking is just beginning to take hold at investment and commercial banks in New York, London, Frankfurt, Tokyo, and other financial centers. Moreover, an environmental checklist is emerging in the green or environmental finance arena, yet another area where financial engineering can bring about market development and liquidity. Forward-thinking and globally based energy participants should embrace the inevitability that international policy on GHGs is being set by both media and public perceptions. In this context, the rational response by enlightened industry participants is to develop and support market-based solutions to global pollution. Since most environmental financial contracts such as SO2 or CO2 are traded on the OTC markets, there is an opportunity for exchanges such as ICE Futures or NYMEX to offer OTC clearing which would effectively make them quasi-futures contracts under government oversight. This could help make them more acceptable to risk managers. ICE Futures already recognized this opportunity in April 2004 and has linked its platform to the Chicago Climate Exchange in order to trade emissions in the EU and, in April 2005, to the European Climate Exchange, the largest of nine European emissions trading exchanges. THE NEW INVESTMENT MODEL FOR THE GREEN SPACE The third year of sustained higher energy prices has finally convinced investors that higher energy prices are here to stay. This acknowledgment is igniting the clean technology sector as environmental issues are also pushing forward. In fact, energy and environment are now fused as an emerging investment sector. Its outcomes cannot be charted, but it is helpful to understand the investment parameters and dynamics of this new sector. Pushed by higher and sustained energy prices, something new has entered the “green financial” space, and that is the arrival of some energy and environmental hedge funds in late 2004. Venture capital wants returns. Hedge funds want those returns even faster. Private equity funds are anxious to invest. The new model is a hybrid of all three (venture capital, hedge funds, and private equity), with a trading dimension thrown in.

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Equity is available. The blurring of the lines between these three business models has given rise to a new hybrid model for the green investment space. It may be helpful to review what has recently been occurring in the “clean technology,” or “cleantech,” space. The time has arrived for green technology. But the space is very different than many envisioned. The mantra of good venture projects for the clean technology space, as with any other venture capital project, is “revenue stream, seasoned management team or repeater CEO, exit strategy.” Some of these technologies are so debt-ridden that they will never be commercially viable. But there are some gems in this arena, particularly for the second-stage investments that do have revenue and will make money for investors. Venture capital funds are increasingly focused on them (see www.cleantech.com). There is plenty of capital available, but few good deals. The timing is now right as higher energy prices are now sustainable due to infrastructure underinvestment and higher demand growth, which will drive return on investment (ROI) higher in the cleantech space. But what about the trading markets and the reduction of project costs? The new model that has emerged is, for lack of a better term, “hybrid”—somewhere between venture capital, hedge funds, and private equity. They require a capital commitment of investors of 2–4 years (called a lockup) and a capability to trade the renewable energy credits and emissions reductions (SO2, NOx, and CO2). These green streams of revenue make the cost of capital more acceptable but also bring liquidity to much-needed emerging environmental financial markets. As stock market analysts like to say, there are many ways to play this theme. So, stocks and bonds of environmental companies or companies that pass through an environmental screen are one way to play it. There is also a growth of index-like products and several exchange-traded funds. There are also insurance and reinsurance products, particularly as weather and climate risk rises. There is much talk about sustainability and sustainable development. This is usually linked to climate change and GHG returns. But once again, this does not get us to ROI criteria that many investors need to make the plunge. There are three main groups of investment that can be followed: institutional investment grade market caps (with some environmental component, however diluted); smaller companies that are usually undercapitalized and under heavy research and development funding (i.e. looking for breakthrough technologies); and venture capital/private equity/hedge fund.

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This group is high-risk and usually reserved for individuals of high net worth who can afford to lose their money. There is another economic argument that is beginning to emerge forcefully in the investment community. Energy and environment are starting to be perceived as a new asset class for investment diversification (including hedge fund investors). It is a means to diversify risk for investors. It is also starting to flow into the agricultural field, the second leading source of global pollution, and water, which is emerging as a commodity market. Some equity and hedge funds are now pursuing this strategy, and expect more to surface during 2006. The green sector includes companies involved in solar, wind, water, and biomass energy, distributed energy (for that matter, distributed water), fuel cells, microturbines, battery storage, metering, and information technology. Ethanol and biofuels excite many people today, but agricultural waste (such as cellulosic ethanol) and fast-growing benign fuels, such as switch grass, are neither exciting nor particularly environmentally benign. In fact, the biggest corporate environmental announcement in 2005 besides GE’s Eco-Imagination was on November 22, 2005, when Goldman Sachs, the premier investment bank in the world, made a corporate statement that it was going green with $1 billion in investment in renewable and energy efficiency, making markets in CO2 emissions and establishing a Center for Environmental Financial Markets. Wall Street sees the money, and it is not altruism that drives them. They see return on investment. CLIMATE CHANGE AS AN INVESTMENT OPPORTUNITY Depletion of the world’s natural resources, coupled with rising global environmental concerns, is creating opportunities are well as risks in the climate change sector. Climate change risk is beginning to have a pronounced impact on financial markets and on corporate performance as shareholder pressure continues. In fact, there are various networks that should be mentioned: the Investor Network on Climate Risk and the Carbon Disclosure Project, with $31 trillion of institutional investor money, are focusing more attention on this issue. While not only raising a corporate board issue throughout the world, it is presenting many investment opportunities, as some companies begin to position themselves to

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offer green products or financial services. These green products or technologies can reduce GHG emissions and other environmental footprints. Expect to see more green mutual funds, exchange-trade funds, green insurance products, green REITs (Real Estate Investment Trusts), and carbon aggregation schemes. On a larger scale, there are clean technology and carbon funds and carbon finance plays under way. These are usually for big institutional investors and not yet yielding returns, although there have been several attempts to sell carbon credits on eBay—a retail investor play if ever there was one. Buy carbon credits and feel good at the same time. CLEANTECH INVESTMENT INDEXES One passive and usually rewarding way to play a sector that has traditionally been used by investors who are less risk tolerant is to use investment indexes. Several cleantech investment indexes are available for today’s investors. These include the KLD Global Climate Index, Powershares WilderHill Clean Energy Indexes, SAM Group Smart Energy Index, Light Green Advisers Eco Indexes, and New Energy Finance’s indexes, to name but a few. The problem is once again to show returns, but it is coupled with a long-term investment horizon and a high risk tolerance for volatility. GET TO KNOW YOUR RISKS The biggest risk for investors is technology risk in many new equities that are beginning to flood the market, particularly the London Stock Exchange’s AIM (pink sheets). Can these new devices work? They can if they pass through the IPO phase to concept stocks. These new companies need to make their numbers. Many are not tracked by anyone, and most are tracked by a few research firms such as Ardour Capital, Jeffries, First Albany, and Piper Jaffray. Thus fiduciary responsibilities and prudent due diligence of institutional investors, such as pension funds that desire to shift into investing in this sector, may be stymied due to the risk factor; moreover, these new companies must show investment returns. Despite the confusing global regulatory picture on climate change, renewable energy, and clean technology, many investors now smell the coffee. There are now both Indian green funds and Indonesian green funds in formation, as well as many more carbon financial funds in both the

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USA and EU. The effort will be global because the solutions are. With the Kyoto Treaty and the EU ETS in place, one more piece of the political puzzle comes into place, and in 2008 the pace of development will start to pick up (the first Kyoto compliance period starts then). As corporate pressure rises on GHG mitigation, the USA will not be left out of this equation as it produces 25% of GHGs and has a carbon footprint of 7.2 billion tonnes, which (according to the latest US Energy Information Administration statistics) increased 2% during 2004. WHY ENVIRONMENT IS RISING AS BOTH A CORPORATE FINANCIAL ISSUE AND INVESTMENT OPPORTUNITY There is a growing realization that complex energy problems will not be fixed by short-term solutions. It has taken Wall Street about a year to finally accept that we no longer have mean reversion in oil prices. The lows get higher. The reality is very different than the illusion. When the paradigm actually breaks, rather than shifts, people just run scared. They deny what is obviously in front of them and pretend it is not there. The focus on energy demand has overshadowed environment as a market. That is now changing. Environment has established itself as the next disruptive factor in energy commodity markets. Environment now overlays the energy value chain. The traders are lining up as green hedge funds magically appeared at the end of 2004. They bring liquidity to markets once again. This change in a new emerging market also brings added complexity to markets since environmental financial markets are really hybrid markets depending on government sanction and markets. Their convergence with capital markets is just beginning. They truly are a second wave that will hit energy trading and the global energy industry way beyond carbon trading. Rising and more stringent environmental standards will cause supply disruptions, price spikes, and more uncertainty than ever before. There will be greater cross-commodity arbitrage opportunities than ever before. The arbitrage will be between oil and gas, coal and emissions, coal and gas, weather and emissions, renewables and efficiency, and so on. This is a new dimension of energy trading, where environment is the new unquantified risk factor—actually many factors. As one Washington, DC, law firm stated, “Pollution Will Reshape US Markets.” That is an understatement.

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The new financial markets of energy and environment will knock out whatever equilibrium people expected to return. The models and templates of the past and the mean reversion expected are gone forever. Green now rises as a disruptive force in markets. The ride is not only bumpy but more risky. Knowledge and experience in energy commodity markets will be one factor in creating the markets, but green project finance in renewable energy, gasification technology, and the like will now escalate (especially with the new energy bill) to create more credit and allowance trading. Ethanol trading will create an entire submarket, as will biofuels. As carbon trading accelerates from only 50 million tons today to billions of tons in 2008, the markets will need speedier thinking. Environment is the disruptive factor. And “green will be good” in both money and environment. The trading opportunity will continue through the end of the decade as the new converging factors of energy, environment, and capital markets increasingly come to the forefront.

C H A P T E R

21

Lessons Learned from the US Experience in Trading Sulfur Dioxide Allowances Richard T. Stuebi

INTRODUCTION By most accounts, the USA is home to the most expansive and successful emissions trading regime ever achieved: the sulfur dioxide (SO2) allowance “cap-and-trade” program as implemented under Title IV of the 1990 Clean Air Act Amendments (CAAA) to address then-escalating concerns about the phenomenon of acid rain. With increasing consideration of analogous trading approaches to reducing carbon dioxide (CO2) emissions to combat the global climate change issue, it is timely to review what has been learned from more than 10 years of experience in trading SO2 allowances. This chapter is structured into three parts: ●

●

●

a summary of the basic regulations and market elements under which SO2 allowances are issued and traded in the USA; a review of the literature estimating the economic and environmental impacts of the US SO2 allowance trading experience to date; a commercially oriented discussion of lessons learned from the US SO2 trading experience and potential implications for CO2 cap-and-trade schemes. 307

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SUMMARY OF SO2 ALLOWANCE TRADING Dissertations have been written on the history of how the acid rain provisions of the CAAA came into being. What resulted from several years of debate, analysis, and compromise during the late 1980s in the Washington, DC, political arena is a lengthy, arcane, and complex set of requirements. Although the regulations have many nuances, they can be summarized in a simplified form without losing their gist in the space of a few paragraphs. The net effect of SO2 reduction provisions of the CAAA was to stipulate that owners of fossil-fired power plants—the main source of SO2 emissions—must by the early twenty-first century reduce their aggregate SO2 emissions by approximately 50%. The requirements were imposed in two phases so as to spread over time the economic burden on power plant owners (mainly electric utilities) of fulfilling the ultimate requirements. ●

●

Phase I, from 1995 through 1999, in which a set of highemitting power plants (263 specifically identified power plant units, selected based on their historically high SO2 emissions) were required in aggregate to emit less than about 5.7 million tons per year, equivalent to an overall emission reduction from this set of units of about 40%. Phase II, from 2000 and continuing indefinitely, capping the aggregate SO2 emissions from all fossil-fired power plant units in the US larger than 25 megawatts (comprising about 3500 power plant units) to approximately 9 million tons annually by 2010, which is about half the aggregate emission levels from the power generation sector in 1980.

The general requirements of Phases I and II are depicted in simplified fashion in Figure 21.1. While most power plant units in the US were not mandated to participate in Phase I of the program, owners of power plant units only mandated for Phase II participation could elect to “opt in” units for Phase I participation at their discretion. As we shall see below, although voluntary emission reductions before required would seem to be an additional economic burden that most power plant owners would want to avoid if possible, “opting in” to Phase I participation in fact occurred to a significant extent. Probably the most important element to note about the CAAA is that both Phases I and II entailed emissions limits that spanned many power plant units. Previously, with very few exceptions, power generation emissions were regulated by state and federal environmental authorities on a

CHAPTER 21 Lessons Learned from the US Experience in Trading SO2

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21.1

Annual utility SO2 emission requirements

Source: US EPA, Ellerman.

unit-by-unit basis, with each power plant generating unit subject to its own limits as set by environmental authorities. The CAAA did not add or tighten any of these pre-existing unit-level emissions regulations but overlaid an additional emission reduction requirement that was binding across many units. It then remained up to power plant owners to make decisions (presumably based on economics and market forces) on which power plant units would reduce emissions by how much, and by what methods, to achieve the overall requirement. This is the essence underlying the concept of emissions trading. For any commodity to be traded, it is first essential to gain mutual agreement upon the accepted and acceptable parameters or characteristics of the commodity itself. In the case of SO2 as addressed by the CAAA, the concept of “allowances” was invented, in which the owner of an allowance was essentially granted the “right” to emit 1 ton of SO2 into the atmosphere. As part of the CAAA, the US Environmental Protection Agency (EPA) is responsible for distributing SO2 allowances to power plant owners nationwide at the beginning of every year. The amount of allowances that EPA issues to each owner annually in Phase II is a function of (1) each power plant unit’s average annual fuel consumption between 1985 and 1987 and (2) the lower of (a) its historical SO2 emission rate (in lbs. per million BTU) during 1985–1987 and (b) 1.2 lbs per million BTU. Through the year 2030, the EPA annually issues about 9 million tons of SO2 allowances to numerous parties nationwide that own power plant units

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covered by Phase II of the CAAA. By the granting of roughly 9 million allowances annually, the EPA thus ensures the capping of SO2 emissions from covered power plants at roughly 9 million tons on an annual average. The owner of an SO2 allowance has several options. Most obviously, it can “use” the allowance by emitting 1 ton of SO2 into the air as a consequence of operating one of its power plant units—either the unit for which it received the allowance or for another of its units subject to the CAAA. Alternatively, it can “bank” the allowance, essentially saving the allowance for later use in future years. More crucially, it can engage in emissions trading and “sell” the allowance to another party. The buyer of an allowance then holds the option to “use,” “bank,” or “sell” the allowance. Indeed, some buyers of allowances have been environmental organizations, who then “retire” bought allowances and thereby enable emissions reductions to fall even further below the aggregate cap established by the CAAA. Allowance transactions can occur “bilaterally”—directly negotiated between buyer and seller. However, bilateral transactions are generally inefficient, with high transaction costs and limited price discovery that would otherwise enable other value-creating trades. (Put another way, in a bilateral transaction, it is more likely that one party or the other is not getting a “fair” deal, relative to market prices.) Therefore, even before the beginning of Phase I, in anticipation of significant trading volumes, overthe-counter markets, futures contracts, and brokerage firms formed to facilitate allowance transactions. As shown below, these markets have grown more robust over time, now with significant liquidity. Without any adverse consequences for non-compliance, there would be no impetus to engage in allowance trading—power plant owners would simply emit whatever they wanted. Therefore under the CAAA, the EPA is authorized to penalize owners of power plants covered by the CAAA $2,000 for every ton of SO2 emissions in a given year exceeding the allowances they remit for that year to the EPA. As a result, the price of allowances is forever effectively capped at $2,000/ton, as a power plant owner can always choose non-compliance in lieu of buying additional allowances. Lastly, it should be noted that 2.8% of all allowances to be granted annually by the EPA are actually withheld by the EPA and auctioned in a market process to encourage trading and provide a corroborating benchmark price for what the market assesses as the value of an allowance. All proceeds from this auction are then returned on a pro rata basis to those parties who would have been allocated these withheld allowances.


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EXPERIENCE TO DATE Given that Phase I of the CAAA was launched at the beginning of 1995, there is now over a decade of experience with which to assess the implications of imposing and running a nationwide cap-and-trade emissions trading regime. Phase I of the CAAA is now over, and Phase II is well under way into its sixth year of activity. A voluminous set of literature has been produced by numerous observers (most notably, Denny Ellerman of MIT and Dallas Burtraw of Resources for the Future) monitoring the developments in this unparalleled environmental marketplace. While a comprehensive review of this literature is far beyond the scope of this paper, some of the more important findings are summarized and distilled into a few pages below. First of all, as one would expect, SO2 emissions from power generation sources have declined in a manner that has been consistent with the emission reduction requirements (Figure 21.2)—even despite steadily increasing output of electricity from power plants. Happily, it appears that the end-goal of the CAAA—to improve the environment and public health—is in fact being achieved, as the reduced emissions of SO2 have resulted in correspondingly significant reductions in key metrics such as ambient SO2 concentrations and sulfate levels. The slight variations in year-to-year SO2 emissions, perhaps not obvious to anticipate beforehand solely based on the structure of the CAAA requirements, can be understood once allowance banking activity (saving allowances for future years) is accounted for.

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21.2

Annual utility SO2 emissions

Source: US EPA, Ellerman.

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Banking allows emissions in certain years to be above levels that might otherwise be expected—as long as there is ample carryover of surplus emission reductions from prior years, which might have been previously pursued and captured because they were unexpectedly inexpensive. As Table 21.1 shows, from the program’s inception, power plant owners emitted less than their allowance holdings and thus began banking surplus allowances for future use promptly. In the early days of the program, based on then-prevailing energy market conditions (in particular, low natural gas prices and an ample availability of excess nuclear and gas-fired generation with zero SO2 emissions), it was relatively cheap for power plant owners to achieve SO2 emission reductions. Power plant owners took advantage of these favorable circumstances and reduced emissions more than absolutely required in order to bank allowances for future use, when allowances prices might be higher. Note from Table 21.1 that allowances allocated annually during Phase I were well in excess of the approximately 5.7 million tons per year statutorily associated with Phase I units in the CAAA. This was for two reasons. ●

●

Owners of more than 200 power plant units only covered by Phase II of the CAAA found it economically advantageous to “opt in” units for at least one year of Phase I compliance, to earn allowances relatively cheaply under the then-favorable conditions and bank them for future use when they would likely be more expensive (as they have indeed become). Many owners of Phase I units took advantage of a bonus clause that provided extra allowances if they were to install pollution-control technologies (typically, scrubbers) and continue the use of high-sulfur fuels, rather than by shifting to lower-sulfur fuels.

Given this eager “overcompliance” in Phase I, a significant amount of banking of allowances has occurred. Table 21.1 reveals that the amount of allowances that have been banked by all parties has generally been approximately equal to one year’s worth of allowances dispensed annually by EPA. However, after peaking at the conclusion of Phase I, the banked “stock” of allowances is now being to be drawn down as market conditions tighten.


T A B L E

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21.1

Annual allowance flows*

1995 1996 1997 1998 1999 2000 2001 2002 2003

Allowances Allocated

Allowances Carried Over

Allowances Available

Actual Emissions

Allowances Banked

8.7 8.3 7.2 7.0 6.9 9.9 9.5 9.5 9.6

0.0 3.4 6.3 8.0 9.7 11.7 10.4 9.3 8.6

8.7 11.7 13.5 15.0 16.6 21.6 19.9 18.8 18.2

5.3 5.4 5.5 5.3 4.9 11.2 10.6 10.2 10.6

3.4 6.3 8.0 9.7 11.7 10.4 9.3 8.6 7.6

*In millions of tons. Source: US EPA.

As discussed further below, with Phase I now over and natural gas prices much higher, the days of very inexpensive emission reductions and corresponding “overcompliance” are gone (at least for the time being), and allowance prices have risen significantly. Thus allowances that were “banked” at a much lower effective cost than current market prices are increasingly being “monetized.” Correspondingly, emissions in the first few years of Phase II have been in excess of the amount of allowances that were allocated. Annual emission levels in excess of annual allowance allocations cannot persist forever—only until the supply of banked allowances is drawn down to zero. While banking of allowances was pursued aggressively right from the outset of Phase I, allowance trading activity was initially sluggish. As noted above, because of then-prevailing energy market conditions, compliance was relatively inexpensive for power plant owners to achieve, often without having to resort to trading. Also, it took some time for utilities (the primary owners of affected power plants) and their regulators to become familiar with the allowance markets and sort through the economic and regulatory implications of trading. After the slow start, however, trading activity has ramped up fairly briskly (Figure 21.3). The market value of the trading activity amounts to several billion dollars per year, although it is difficult to quantify precisely.

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F I G U R E

21.3

Allowance trades between parties

Source: US EPA.

By most accounts, there is now substantial liquidity in the allowance trading marketplace, albeit with some seasonal spurts and slackness. Once the first few years of both Phases I and II passed, trading volumes diminished somewhat, probably because the significant accumulations of banked allowances reduced the imperative for power plant owners to engage in trades with other parties. However, given the drawdown in banked allowances, one might expect allowance trading volumes in coming years to rebound to higher levels. It is difficult to separate the discussion of allowance trading activity from a discussion of allowance prices and corresponding actions taken by power plant owners to achieve SO2 emission reductions. By economic theory, in an efficient market, pricing for allowances is not determined by the average cost of all actions undertaken to achieve compliance, but rather by the incremental cost associated with the “marginal” action undertaken to achieve compliance. Thus the pricing of allowances indicates only the most expensive action undertaken to achieve compliance. Average costs of compliance are lower than the market price of allowances, and profits from trading can accrue to those parties who undertake overcompliance actions that cost less than the market price of allowances and sell their excess allowances in the market at the prevailing price. As the market launched in anticipation of Phase I in the mid-1990s, the first few trades (in forward markets, transacted in 1992) occurred at almost $300/ton, falling to a low of $66/ton in early 1996 and then trading in a relatively narrow range of $100–200 per ton through the early years of Phase II. According to research by Denny Ellerman and Florence


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Dubrouecq of MIT, about 80% of the gross emission reductions achieved during this period involved reducing emission rates at individual power plants (by either installing scrubbers or switching to lower-sulfur coals), with the remaining 20% reduction stemming from shifting utilization from higher- to lower-emitting units (mostly within the population of coal units, although some shifting occurred to gas and nuclear as well). Unfortunately, no analysis has apparently been conducted to ascertain the then-prevailing marginal compliance activity that set the market for allowance prices between $100 and $200 per ton. Since late 2003, during the middle of Phase II and without any material tightening of emission reduction requirements, allowance prices have increased dramatically (Figure 21.4). This run-up in allowance prices has occurred for the following reasons. ●

Increasing reliance on natural gas power plants to achieve emission reductions is now much more expensive to undertake

F I G U R E

21.4

SO2 allowance spot prices

Source: Evolution Markets LLC.

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as delivered gas prices have almost tripled from their 1995 levels of roughly $2/mmBtu. With gradual ramp-up of capacity factors, from about 70% in the early 1990s to nearly on 90% in the early 2000s, there is relatively little incremental opportunity to further increase further utilization at nuclear power plants. In response to both of these factors, and also because of accelerated rates of depletion during the past 10 years to meet CAAA requirements most cost effectively, the prices of lowersulfur coals (especially coals mined in the eastern US, where most of the affected power plants are sited) have risen meaningfully. The 2005 promulgation of the Clean Air Interstate Rule (CAIR) by the EPA effectively halves the number of SO2 allowances that will be allocated in the eastern US after 2010, thereby making each currently available allowance that much more valuable in the future.

The fact that allowance prices have increased substantially is not a negative judgment on the effectiveness of the allowance trading regime—it simply means that achieving the required emission reductions in aggregate is far more expensive than was the case previously. To assess the economic effectiveness of the trading program, it is more relevant to estimate the economic savings being produced relative to a “no-trading” scenario. The ability for power plant owners to engage in trades to achieve the desired aggregate emission reductions has been estimated to generate more than $2 billion per year in annual economic savings on a national basis during Phase II (Figure 21.5). Furthermore, note that estimated economic gains from trading are about seven times greater in Phase II than in Phase I. This is because the incremental costs of emission reductions across the economy will generally be rapidly increasing as reduction requirements become more stringent (i.e. a steeply increasing “supply curve” of emission reduction alternatives). As a result, the economic savings owing to trading (relative to “no-trading”) will generally increase as allowance prices rise. Not only is the trading program generating significant economic benefit relative to what would be the case without trading, but the trading program is helping reduce compliance costs of the CAAA far below expectations that prevailed before its implementation.


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21.5

Economic gains from trading

Source: Ellerman et al., Emissions Trading in the U.S., Pew Center (2003).

Generally accepted economic projections, made during the late 1980s by various parties in support of the then-raging acid rain political debates in the US, suggested that $2.6 billion in annual compliance costs would be incurred by electricity consumers during Phase II of the thenproposed CAAA. Given that actual compliance costs are currently estimated to be on the order of $1.4 billion per year, original forecasts of compliance costs turned out to be considerably higher than the compliance costs that are actually being incurred today. Put another way, actors in the marketplace are finding ways to comply with the CAAA that are considerably more efficient than had been expected—and the commercial incentives for achieving these gains can in large part be attributed to the allowance trading program. The overall verdict from the literature is therefore clear: while no one would argue that all has gone perfectly during CAAA program implementation, the SO2 allowance trading regime in the US over the past 10 years has been a clear success on both environmental and economic grounds. LESSONS LEARNED In their analyses to date, the highlights of which are summarized above, economists have played a highly valuable role in providing objective

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quantitative assessments of the SO2 allowance trading experience in the US. Additionally, their work has included conclusions of “lessons learned” from this historical experience with SO2 allowance trading, as recommendations to be applied in crafting CO2 trading schemes. There has been substantial activity already on this front. A capand-trade structure involving CO2 allowances is already embedded in the European Union’s Emissions Trading Scheme (ETS), which was implemented on January 1, 2005, to enable EU countries to achieve compliance with their commitment to the recently binding Kyoto Protocol. A regional CO2 cap-and-trade scheme is currently being developed in the US northeastern states as part of the Regional Greenhouse Gas Initiative. Furthermore, many observers believe that any national CO2 mitigation approach in the US is very likely to entail a cap-and-trade program, given the introduction of climate change bills in the US Congress (e.g. Lieberman/ McCain, Carper) that include a cap-and-trade structure for CO2. The conclusions from the pre-existing literature on the US SO2 allowance trading experience have clearly shaped and continue to inform policy debates on the CO2 trading structures and proposals mentioned above. For the most part, however, the conclusions about SO2 trading activity put forth to date have tended to shy away from providing useful insight for private actors in the market seeking to maximize shareholder value in planning for and operating under a CO2 cap-and-trade program. Thus this chapter concludes with a list of observations intended to help industry participants be most successful under a CO2 emissions capand-trade regime. Some of these key lessons learned are as follows. ●

The worst fears of doomsayers may turn out to be unfounded when reduction requirements are actually imposed. When the US acid rain debates were raging in the late 1980s, and the concept of a “cap-and-trade” system with an absolute ceiling on aggregate SO2 emissions was initially floated, some opponents (primarily high-emitting utilities) alleged that the fixed cap on SO2 emissions would be a constraining factor limiting the ability of the electricity industry to ensure that the lights would always be kept on—conversely, that the cap would have to be violated in order to accommodate continuing growth of electricity demand. To alleviate this concern, as part of the final CAAA provisions, the EPA is authorized sell a “standby supply” of allowances to any buyer at $1,500/ton—essentially a “relief valve” in case the economic or operational pressures on the


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“cap-and-trade” regime become too significant to bear. This “emergency” auction has never yet been invoked, and it appears to be a long time (if ever) before it will be, as allowance prices in the market have now closely approached $1,500/ton, even under energy market conditions and material regulatory changes (e.g. the development of CAIR) that had not been anticipated at the time. Thus if certain parties argue that future compliance under a CO2 cap-and-trade scheme will be impossible to achieve, note who is making claims of great impending adversity, and consider their motivations for making such strong statements. Technological and commercial innovation is likely to be greater than expected. During the policy debates of the late 1980s in establishing the CAAA, some observers took a less extreme opposing position but were nevertheless quite concerned that compliance costs would be very high. Two causes for concern were stated: (1) some doubted the supply and availability at reasonable cost of lower sulfur fuels (primarily low-sulfur coal, but also natural gas) and (2) the then-current costs of scrubbers—the predominant pollution control technology to reduce post-combustion SO2 emissions—was projected to remain fairly high. However, neither of these sources of cost pressures materialized as strongly as some had worried. Why were costs not as high as expected? Simply put, market forces have been a powerful driver for innovations that have resulted in reduced compliance costs—more powerful than economic forecasters could ever have defensibly projected. As for the first concern, very inexpensive and plentiful low-sulfur coals from the Powder River Basin in Wyoming (though initially thought to be unsuitable for burning in many power plants not designed at inception for their use) have been utilized effectively more widely than generally anticipated, stemming from creative solutions invented by power plant engineers who were unleashed from the constraints of “command-and-control” compliance. As for the second concern, reflecting the first significant production of a technology that was just beginning to achieve maturity, scrubber costs have traveled rapidly down the “learning curve,” falling by over 50% in the 15 years since the CAAA was debated (Figure 21.6).

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F I G U R E

21.6

Scrubber capital costs (for “wet” scrubbers)

Source: Depriest, In Emissions Trading, Kosobud ed. (2000).

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Certain structural elements of the CAAA SO2 trading allowance program are likely to be employed in any CO2 trading program. Clearly, for a cap-and-trade program to be effective, there must be economic penalties imposed by the environmental authorities in the event of non-compliance, and these penalties serve as a ceiling on allowance prices. Also, because of the widely acclaimed successes of the CAAA, it is hard to envision a CO2 cap-and-trade scheme being implemented that does not include multiple phases with successively tightening emission reduction requirements, as has been adopted in the EU ETS. More speculatively, there is a compelling case for banking of early reductions to be allowed because they encourage emission reductions sooner—and at lower aggregate economic cost. Today’s operations can set the baselines by which future operations may be limited. Most of the main provisions of the CAAA use historical emissions and fuel consumption data from 1985 to 1987 in establishing the all-important allowance calculations. Put another way, tactical day-to-day operational decisions made at the power plant unit level now fully 20 years ago have permanently set the field of play for the owners of these power plant units. This means that in advance of CO2 trading regimes, owners of major emission sources should be thoughtful so as not to fall into unusual operational patterns that


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may later cause regrets just because the actions taken at the time were expedient in meeting a short-term situation. Participating in the up-front shaping of emission requirements is likely to be advantageous. Observers examining the CAAA in full detail will notice that there are many idiosyncratic instances of lessened requirements and outright exemptions (otherwise known as “loopholes”) for power plant units with certain combinations of seemingly irrelevant attributes. These provisions resulted from concessions and compromises reached in the final debates—those parties who were “at the table” and constructively participated in the deliberative process were far more likely to have their concerns heard and addressed to gain a break. Conversely, those that were absent from shaping the legislation were subject to the general provisions, not receiving any special dispensations that could have reduced the economic burden of compliance. For the climate change issue, the lesson is that those parties who choose to ignore or dismiss any regulatory debates could later rue their non-participation. The manner in which allowances in a “cap-and-trade” system are allocated will matter greatly. Each allowance is essentially a “gift” provided by the government, so more allowances are better. Of course, the government cannot be seen to arbitrarily increase the number of allowances granted to various recipients, so the formula by which allowance amounts are calculated is of extreme importance. This will be as true of a potential future CO2 trading regime as it has been of the SO2 allowance trading scheme. As noted above, several industry participants were very aggressive in their positioning for additional allowances through the introduction of seemingly minor passages that contained arcane loopholes. Additionally, the impact of including/excluding methane and other greenhouse gases, and at what conversion factors to CO2 “equivalents,” will also be a significant issue and one that the US experience with SO2 allowance allocation cannot directly address. A vast array of financial derivatives and speculators will emerge. Once a spot market for a commodity forms, a host of derivatives inevitably follows: forwards/futures and options enable participants in the market to lock in or hedge their positions,

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rather than being forced to wait for future price levels to emerge before determining their ultimate economic/ financial fate. In parallel, markets beget speculators—parties buying and selling the commodity without any obligation to do so, because they are willing to make a “bet” on future price movements (and turn a profit from them). Speculators are beneficial because they help in creating trading volumes (i.e. liquidity) and hence economic efficiency in markets. Further, since most speculators transact in the derivative markets (not taking “delivery” of the commodity in the spot markets), and because there are many more potential speculators in the population than industry participants, the derivative markets tend to experience higher volumes of activity than the spot market—even though the spot market is the “fundamental” market for the commodity, from which all assessments of current and future value are made. Without either derivatives or speculators, a commodity spot market will dry up and die. It takes time to become familiar with trading strategies and tactics. As noted above, the allowance trading market did not instantly achieve significant trading volumes. Instead, power plant owners were largely self-reliant (aided by favorably low costs for reducing SO2 emissions at the time) and banked allowances. They took the time to study the pricing dynamics of the market, the ways in which allowances were transacted, before trading in significant volumes. It is likely that any CO2 trading scheme would be subject to a similar delay in activity, as participants evaluate the marketplace from the sidelines, making only tentative initial forays into the market. With large information and education differences between parties, any new market is more inefficient than mature markets, so expect wide bid–ask spreads in early “thinly traded” CO2 markets (such as those relating to voluntary CO2 trading, which are already active in the USA today). Over time, with increasing volumes and accumulated experience, the markets will become more efficient, as more players engage in economically savvy trading activity. Early emission reduction actions are often cheaper, so voluntary opt-ins can be beneficial. It is logically easier and cheaper to make a smaller amount of reductions than a greater amount, and since it is likely that CO2 trading schemes will have multiple


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phases of increasing stringency, this means that earlier compliance will be cheaper than later compliance. Even if a CO2 reduction requirement were to be imposed all at once, some of the compliance measures are likely to be less scarce (and therefore less expensive) in the early years of the program— consider the possibility of a migration to lower-emitting natural gas in advance of expectations of later price escalation as gas supplies tighten. Thus (assuming that the CO2 cap-and-trade scheme includes banking) those parties that can make more reductions than necessary in the early years may find future economic advantage in doing so. Lower-polluting fuels may be the primary means of reducing emissions under modest requirements, but the use of cheaper, higher-emitting fuels may persist or rebound if and when requirements tighten further. This has been the case under the US SO2 trading experience, but it could also be the case for CO2 trading—particularly if reduction requirements are implemented in phases: in the power sector, utilization shifts from coal to natural gas, nuclear, or renewables could be the preferred approach for initial (lesser quantities of) CO2 reductions, but if and when requirements tighten, a reversion to coal—in concert with gasification and sequestration technologies—might be expected to occur. Regulators setting electricity prices will likely need substantial assistance in understanding the final details of the emissions trading regime. A subset of the literature analyzing the experience of the US SO2 allowance program has been devoted to the issue of regulatory impediments to appropriately recognizing the costs of and economic value created by trading of allowances, which has been argued to be one of the causes of slow initial adoption of allowance trading in the mid-1990s. It appears that regulators were even less informed than power plant owners about the specific requirements and structure of the CAAA and, amplified by their lack of day-to-day involvement in the compliance markets, were thus even slower to become familiar with the trading opportunities and implications thereof. It is plausible to surmise that this would also be the case for CO2 reduction requirements and trading structures.

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Good accounting and administration will be vital. In tracking and trading literally millions of allowances every year for the CAAA, power plant owners and the EPA had to develop significant information systems infrastructure—including measuring devices and data logging/transfer mechanisms. Fortunately for US players, it is likely that some of this same infrastructure can also be used for CO2 under a cap-and-trade scheme. Also fortunately, technology for information gathering and management continues to improve in performance and fall in cost. However, there are likely to be nuances of the CO2 trading program that require either modification to the accounting/administration systems used in the US for CAAA compliance or clean-sheet development of new ones—and such efforts should not be given short shrift, either financially or strategically.

Those who are or may become subject to a cap-and-trade scheme for CO2 emissions would be well advised to study and learn these general lessons gained from the US experience in SO2 allowance trading. REFERENCES Burtraw, D (1998) Cost Savings, Market Performance, and Economic Benefits of the U.S. Acid Rain Program, Discussion Paper 98-28-REV, Resources for the Future, Washington DC, September. Burtraw, D and Mansur, E (1999) The Effects of Trading and Banking in the SO2 Allowance Market, Discussion Paper 99-25, Resources for the Future, Washington DC, March. Burtraw, D, Evans, DA, Krupnick, A, Palmer, K, and Toth, R (2005) Economics of Pollution Trading for SO2 and NOx, Discussion Paper 05-05, Resources for the Future, Washington DC, March 2005. Carlson, C, Burtraw, D Cropper, ML, and Palmer, KL (2000) Sulfur Dioxide Control by Electric Utilities: What Are the Gains from Trade?, Discussion Paper 98-44-REV, Resources for the Future, April. (Also published in Journal of Political Economy, 108(6), pp. 1292–326.) Ellerman, A D (2003) Ex Post Evaluation of Tradable Permits: The U.S. SO2 Capand-Trade Program, MIT-CEEPR Working Paper 2003-003. Ellerman, AD et al. (2000). Markets for Clean Air: The U.S. Acid Rain Program (New York: Cambridge University Press). Ellerman, AD, Joskow, PL, and Harrison, D Jr. (2003) Emissions Trading in the U.S.: Experience, Lessons and Considerations for Greenhouse Gases (Washington, DC: Pew Center on Global Climate Change).


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Ellerman, AD (2003) Lessons from Phase 2 Compliance with the U.S. Acid Rain Program, Working Paper, Department of Applied Economics, University of Cambridge. Ellerman, AD and Dubroeucq, F (2004). The Sources of Emission Reductions: Evidence from U.S. SO2 Emissions From 1985 Through 2002, Working Paper, Department of Applied Economics, University of Cambridge. Evolution Markets, www.evomarkets.com. Global Change Associates, www.global-change.com. Kosobud, RF (ed.) (2000). Emissions Trading: Environmental Policy’s New Approach (New York: John Wiley & Sons). Shadbegian, RJ, Gray, W, and Morgan, C (2004) The 1990 Clean Air Act Amendments: Who Got Cleaner Air—and Who Paid for It? Working Paper, Association of Environmental and Resource Economics 2004 Summer Workshop, Estes Park, CO. US Environmental Protection Agency (2004) Acid Rain Program: 2003 Progress Report, EPA 430-R-04-009.


C H A P T E R

22

The Complexities of Trading Regional Emission Markets Randall Lack

INTRODUCTION The environmental credits market is one of the fastest growing sectors of this decade, and new legislation will grow the emissions trading business by at least 500% by 2012 in the USA. There are 35 emissions trading regions in the USA (see Table 22.1), each with its own rules, market drivers, and pricing. This chapter attempts to describe one of the most difficult tasks facing environmental compliance officers today, keeping up with the compliance regulations, including prices, in their respective regional emissions markets. An environmental Health & Services manager of a Fortune 1000 company, if one exists, will typically handle emissions compliance for all of the company’s facilities within the USA, which could result in as many as 20 different trading regions, rules, and pricing. For the first time in history, environmental managers are not being associated with the cost side of the equation alone; they are actually being seen as a revenue generator for the company through credit creation and through efficiency projects. These new duties are ones that no single person can or should handle alone, especially without the proper financial training. To illustrate the differences among emissions trading regions, the following two sections describe the Houston/Galveston Mass Emissions Cap and Trade (MECT) Program and the San Joaquin Valley Emission Reduction Credit (ERC) Trading Program, which govern the trading of 327

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emissions from stationary sources only. You may be asking yourself, “What do I care about the Houston/Galveston or San Joaquin Non-Attainment areas?” The reason for the comparison is to show how emissions markets can differ dramatically, and this scenario may continue even among the Greenhouse Gas Markets within the USA. A more subtle point is also revealed by describing how lobbying groups can have an impact on the resulting regulations. With programs (whether voluntary or mandatory) popping up around the USA to develop a trading scheme for CO2, such as the Regional Greenhouse Gas Initiative, the California Climate Action Registry, and the Chicago Climate Exchange, we could end up with a CO2 trading market that looks more like the regional emissions markets that exist today rather than a national market or the international market intended by the Kyoto Protocol. Table 22.1 lists the emission trading regions and some idiosyncrasies unique to those regions.

T A B L E

22.1

US emissions trading regions Emission Reduction Credit Trading Regions 1.

South Coast (CA)—ERC and Allowance (RECLAIM)

2.

San Joaquin (CA)

3.

Bay Area (CA)

4. 5. 6.

Ventura (CA) Imperial (CA) San Diego (CA)

7. 8.

Monterey Bay (CA) San Luis Obispo (CA)

Idiosyncrasies ERCs are transferable to Mojave and Antelope. Priority Reserve has been established to make PM10, SOx, and CO ERCs available to new power generation developments. RECLAIM is an annual reconciliation program for NOx and SOx. Credits may be transferable to downwind air districts. ERCs are expected to be RACT upon use. Precursor Organic Compounds (POC) are useable for NOx on a 1:1 ratio. SOx are useable for PM10 at a 3:1 ratio. NA NA Allowed NOx credits to be generated from mobile source reductions for the Otay Mesa Power Project (Calpine). NA NA

CHAPTER 22 The Complexities of Trading Regional Emission Markets

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22.1

US emissions trading regions—cont’d 9. Mojave Desert (CA)

Very limited on credits. Most projects are permitted by using South Coast credits. 10. Shasta County (CA) Can transfer to downwind affected sources. 11. Yolo Solano (CA) Can transfer to downwind affected sources (including Sacramento). Many of the ERCs were from agricultural burning mitigation but are no longer useable by major sources. 12. Butte County (CA) Credits are tradable into the region from upwind sources and are tradable out of Butte to downwind sources. 13. Placer County (CA) Rice burn mitigation credits are usable for offsetting new emitting sources. 14. Santa Barbara (CA) Credits that are moved from the northern region to the southern region must be offset at a 4:1 ratio. 15. Sacramento (CA) Will allow the importation of credits from within 50 miles, except for PM10 if a majorsource. 16. Michigan NA 17. Connecticut (severe/moderate) Credits can transfer to and from New York. 18. Maryland (severe/moderate) Credits can transfer to and from Pennsylvania. 19. Pennsylvania (severe/moderate) Credits can transfer to and from New York and Maryland. 20. New Jersey NA 21. New York (severe/moderate) Credits can transfer to and from Pennsylvania and Connecticut. 22. Indiana NA 23. Wisconsin NA 24. Arizona NA 25. Utah NA 26. Baton Rouge (LA) NA 27. Massachusetts NA 28. Houston/Galveston (TX) 80% reduction in NOx mandated by 2008 from 1997–1999 baseline years. ERCs are only used for environmental retirement. 29. Dallas (TX) NA 30. Beaumont/Port Arthur (TX) NA 31. Illinois Allotment Trading Units (ATUs) are traded for Volatile Organic Materials (VOM). Annua reconciliation is required.

Continued

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T A B L E

22.1

US emissions trading regions—cont’d 32. Atlanta (GA) 33. Kentucky 34. SO2 Acid Rain Program

35. NOx SIP Call Program

NA NA The most liquid trading program in the country. Credits can be transferred from any affected power generating unit to any other affected source within the US. Annual reconciliation is required. NOx SIP Call will soon be replaced by the Clean Air Interstate Rule (http://www.epa. gov/cair/).

Source: Element Markets LLC

HOUSTON/GALVESTON MASS EMISSIONS CAP AND TRADE (MECT) PROGRAM The Houston/Galveston MECT Program began January 1, 2002, in order to meet the State Implementation Plan (SIP) for the one-hour ozone standards. The program has proven to be one of the most demanding in the USA, calling for an 80% reduction in NOx from 1997–1999 baselines. The mandatory reductions are for any source that has a potential to emit more than 10 tons of NOx annually. Currently, there are over 200 facilities in the MECT Program. When the program was originally unveiled, it called for a 90% reduction in NOx, but due to a successful lobbying effort, assembled by the law firm Baker Botts through the Business Coalition for Clean Air Appeal Group (BCCA), the rules were changed. The BCCA was an aggregate group of the biggest players in the Houston/Galveston area, which worked with the Texas Commission on Environmental Quality (then known as the TNRCC) and negotiated to reduce the mandated reduction to approximately 80%, depending on the rating of the equipment.1 Offered up as a bargaining chip were highly reactive organic compounds, which will be speciated in the Houston/Galveston area for the first time ever. The program was scheduled to go into place in 2006. The proposed allocations have been released, however, it is expected the program will be delayed due to lawsuits attempting to eradicate the program.


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22.1

Example of Houston/Galveston NOx allowance stream allocation

Source: Element Markets LLC.

The trading scheme has been designed as a step-down program (see Figure 22.1), where years 2002 and 2003 are set as the baseline years, and reductions are targeted every year through 2008. The 2008 allocation has become known as the perpetual allocation, which is an allowance of credits from 2008 on, barring any rule changes or shaving of the program. In addition to the allowance trading program, any new source greater than 40 tons must offset 15% of a facility’s permit levels with ERCs, for the benefit of the environment, and they must provide a 1:1 ratio of allowances to cover their actual emissions. There has been over $250 million in trades since the inception of the MECT program. The market emerged to bullish predictions by ICF Consulting predicting “California Style” pricing over the next 5 years for the NOx market (this prediction was based on the comparison to the California pricings of $100,000+ per ton during the California Energy Crisis). The market did emerge with a large amount of trading activity due to shutdowns in the region. However, many of the shutdowns that occurred were due to economic decisions from methanol and petrochemical operation rather than allowance pricing. Prices for the perpetual allowances streams in the first two years of the program ranged from $27,000/ton to $40,000/ton.

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The main emitters in the Houston/Galveston area are refineries, pipeline compressors, chemical facilities, power plants, and manufacturing facilities. The majority of the emissions in the area are attributed to the refineries and chemical facilities, with the exception of a few power plants, who are also significant contributors. Owing to the large number of point sources of emissions from refineries and chemical facilities, these facilities are often faced with expensive solutions to meet the 80% reductions. In addition, many of the refiners in the region, due to high margins in recent years, have been slow to implement projects to reduce emissions due to postponing turnaround times. Moreover, many of the facilities are running at capacity utilizations higher than the baseline year, which has resulted in an increase in demand for additional technology and/or credits to satisfy their emissions compliance. Power generation sources are one of the major sources of excess credits in the MECT Program, besides shutdowns. Power plants have a greater ability to make the reductions due to fewer point sources on site and a great wealth of effective technologies, including selective catalytic reduction systems (SCR) and selective non-catalytic reduction systems (SNCR). SCR Systems have allowed power generation facilities, like the Parish facility now owned by NRG Energy from the acquisition of the Texas Genco assets, to reduce past their required levels. The Parish facility made 88% reductions in NOx emissions by March 2004 and had spent in excess of $700 Million in emissions controls by June 2004.2 The market drivers in this region have been largely based on the hedging costs of technology. During the early stages of the program, many players were simply evaluating the cost of credits versus the cost of technology and efficiency projects to make decisions about the treatment of their emissions portfolios. Early movers were able to avoid the costs of credits during the initial step-down in the program and were actually able to sell credits in the early years (2002–2006) to help hedge the costs of equipment. The long-term projected feedstock costs have also had a significant impact on facilities’ decision whether to continue operating, shut down, switch feedstock, or move overseas. In one case, a large facility commented, “It is cheaper for us to move our operation to Saudi Arabia where gas is a lone dollar per gallon and face no environmental regulations.” In addition, the corporation received significant revenues from the sales of emission credits and their remaining feedstock contracts, both helping to support the decision to move overseas.


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The Houston/Galveston area market has evolved into a very illiquid and idiosyncratic market. Typically, it will face periods where the market will not trade for nine months, followed by significant action over a period of one to two months. There continues to be a shortage of supply of credits in the region, and many groups are unable to meet the stringent requirement deadlines by 2008. These supply restrictions have further caused prices to skyrocket in the recent months in this market. The last trade seen in the market was in excess of $70,000/ton for the perpetual allowances and prices are believed to continue in this upward direction. The bullish predictions of the past seem to be coming true, which could be very painful to many of the refiners and manufacturers in the region that did not take early action to mitigate their risk. SAN JOAQUIN VALLEY EMISSION REDUCTION CREDIT TRADING PROGRAM The San Joaquin ERC Trading scheme is significantly different from the Houston/Galveston MECT Program but is very similar to many of the ERC trading programs seen elsewhere in the USA. The program, which began in September 2001, trades NOx, volatile organic compounds particulate matter under 10 microns (PM10), and sulfur dioxide (SO2), but for the purpose of uniformity this discussion will focus on NOx and the comparison to the Houston/Galveston Area NOx SIP Call Program. The ERCs issued in the region are measured in tons per year for the life of a facility and do not have expiration dates. In order to be issued a permit in the region, a facility must offset the potential to emit above the threshold levels stipulated for the region (the NOx threshold is 10 tons per year per facility, which was reduced from 25 tons under the new “extreme” designation in 2004). In addition to offsetting the potential to emit, a facility must also retire 50% above its required amount for the benefit of the environment. If the ERCs are generated within 15 miles of the facility they are being applied against, the offset ratio decreases to 20%. This is a rarity, unless a company has banked ERCs from its own facility. For instance, if your facility has the potential to emit 40 tons/yr, you must purchase 45 tons/year of offsets to satisfy your requirements if the ERCs are from a source outside of the 15-mile range (30 tons above the offset threshold of 10 tons plus 50%). If the credits were generated within the 15-mile range, you would only be required to offset the permit with 36 tons (30 tons above the offset threshold plus 20%). Should the facility have a

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reduction in emissions or potential to emit for any reason, and once the facility is permitted, a portion of the credits put toward the permit will be refunded to the facility owner but will be adjusted based on best available control technology (BACT). Since 2001, NOx ERCs have ranged from $3,000 to over $60,000 per ton. The wide range of prices is mostly attributed to the California energy crisis, during which time there were three or four companies or municipalities at any time competing for credits, and prices increased over 1,500% in nine months. During the peak of the energy crisis, it was not uncommon for a company to outbid itself for the same credits due to the excessive competition and lack of transparency. This hysteria was exacerbated when those same companies submitted multiple bids through different brokerage shops. The insanity of that time was a large reason for the jump in prices from the low in 2000. This scenario was common throughout California, especially in Sacramento, South Coast (the Los Angeles Air Basin), and the Bay Area. The San Joaquin trading region has been through some difficult times lately due to the fact that it is one of the dirtiest air basins in the USA and was reclassified in 2004 as being in “extreme” non-attainment of federal one-hour ozone standards. Previously, the district was classified as “severe” non-attainment, which carried a compliance deadline of November 15, 2005. Failure to meet the attainment deadline would result in a loss of billions of dollars in transportation funding and impose fees on the San Joaquin Valley businesses. The recent turbulence over the compliance with the State Implementation Plan and regional designation has had an impact not only on pricing, but also on the ways ERCs trade. The San Joaquin market, if it fails its equivalency, will move to a Reasonably Available Control Technology (RACT) adjustment upon use. In the RACT upon use system the purchaser of credits will have the credits reviewed and reduced under the current RACT rules. This will reduce the supply of available credits in the region by approximately 90% because many of the available credits in the region were either from refineries or manufacturers that were shut down as far back as 1978. Over the past three years, NOx credits have traded between $20,000 and $22,000 per ton, but trades are infrequent. Prices seem to be gaining traction given the proposed RACT adjustment, and there are expected to be around four prospective buyers in the San Joaquin NOx market in 2007 for new power and petrochemical developments or expansions. On the


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basis of the supply and demand profile of the region, the anticipated rise in prices is justified. The only new supply to the market is expected to come from the agricultural sector shutdowns resulting from the recent doldrums of the business environment in California (as illustrated in Figure 22.2, a large part of the emissions in the region are from agricultural sources). The market drivers, given the way the ERC trading scheme works, differ dramatically from those of the Houston/Galveston MECT Program. The market is primarily driven by new grass-root facilities and expansions. Additionally, capacity utilization does not affect the trading at all, as it does in the MECT Program, due to the fact that the facilities are permitted based on maximum potential to emit. The primary purchasers since 2003 have been municipalities trying to hedge their exposure to another energy crisis by developing baseload or peaking facilities. Examples of this activity are observable in the Turlock Irrigation District, Modesto Irrigation District, and Kings River Conservation District. The petrochemical refineries in the Bakersfield area, on the other hand, have large expansions coming online due to strong margins from refinery operations. Speculative interest is expected to increase over the next two years due to the uncertainty in the market and an expected increase in trading volumes. The significant legislative risk has also impacted the value and trading of the ERCs in the San Joaquin Air Basin. Should San Joaquin move to the RACT upon use system, we will see a run-up in prices of RACT adjusted

F I G U R E

22.2

Emissions of reactive organic gases and oxides of nitrogen

Source: Estimated by California Air Resources Board.

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credits since it will constrain the supply of credits available for offsetting new developments. The non-surplus credits (credits that are RACT adjusted to zero) will be useable and valid for minor sources, such as packaging, petrochemical, and wastewater treatment facilities, however, they will lose at least 75% of their value. As these two examples illustrate, when evaluating a trading region, it is important to understand that not all trading regions are equal. Often, the market drivers, participants, and rules are vastly different from region to region. Whether it is San Joaquin ERCs or the European Union Emissions Trading Scheme CO2 being evaluated, it is important to understand which factors are affecting the emissions market in that region. Understanding these idiosyncracies is fundamental to the ongoing compliance and operations of a facility. Further, when developing a corporate compliance strategy, it is important to manage your emissions portfolio as one would manage a commodity or securities portfolio. NOTES 1. Equipment is rated according to an Emissions Specification for Attainment Demonstration (ESAD). The ESAD rating is used to determine the reduction allowance for that facility. 2. Ben Carmine, Director, Environmental Operations, Texas Genco, Industrial Source NOx Reduction Technologies and Strategies Symposium, August 27, 2004.

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Climate Risks and Electric Utilities Carla Tabossi

INTRODUCTION Over the last few years, US business leaders and the financial community have demonstrated an increased interest in risks and opportunities driven by climate change. Compelling scientific evidence, unprecedented adverse consequences from hurricanes, and the emergence of carbon trading as an innovative source of revenue have all contributed to this trend. While providing a highly valuable commodity, electric power companies have a large impact on public health and the environment. The use of fossil energy is the largest source of air pollutants, including humaninduced carbon dioxide (CO2), in the USA and worldwide. The electric power industry accounts for about 40% of these emissions in the USA and 10% worldwide. As such, this sector is a primary target under impending government regulations, which will materially affect many companies’ shareholder values. Operational risks of electric power producers stem from projected changes in climates worldwide with the potential of causing physical damage to power assets, as evidenced by hurricanes in the USA, and unpredictable weather patterns, which affect power consumption. Experts agree that climate change will impact the availability of water for power plant cooling, cause unpredictable variabilities in heating and air conditioning loads, and affect the frequency and severity of extreme 337

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natural events.1 This, in turn, will have significant negative economic, environmental, and human health impacts. Entergy, Centerpoint, and American Electric Power, for example, have suffered significant physical damage as well as lower demand due to Hurricanes Katrina and Rita. These companies will have to make substantial investments to restore their service and face the uncertainty of recovering these costs, which will be bearded by shareholders, electricity consumers, and taxpayers. Although all electric power producers, on average, have a high level of carbon intensity, Innovest’s work found wide variations in carbon exposure and management strategies with the potential to create or erode shareholder value in this industry. Just three companies in the S&P500 US electric power sector account for more than one-third of the industry’s total CO2 emissions while producing about a quarter of the aggregated power: American Electric Power, Southern Company, and Xcel Energy. These companies are among the most carbon-intensive generators in the country, releasing about 30% more CO2 per unit of power generated than the industry average. Estimates of potential compliance costs under conservative scenarios range from zero to 5% of market cap per company, assuming a price of $30 per ton of carbon and a 10% emissions reduction target. The implications of climate change are expected to materially impact corporate returns and shareholder value both through increased capital expenditures and emerging profit opportunities. Even if not convinced that man-made CO2 emissions are causing climate change, managers of fossil fuel–based power companies need to assess the potential implications of climate change in light of the increased investor risks, which raise public scrutiny and impact the cost of capital. As investors increasingly consider companies’ individual carbon risk exposures and management capabilities as part of their fiduciary obligation, major CO2 emitters will face higher debt charges from conscious lenders, higher insurance premiums, and a limited access to both mainstream and socially responsible funds. Although CO2 emissions remain unregulated in the USA, electric power companies face increasing pressures to lower their emissions. Increasing awareness among citizens, legislators, and shareholders of the implications of climate change is now converging, calling for prompt corporate action and prudent investment. More than half of Americans believe that the effects of global warming have already begun, and another 18% believe the effects will be felt in their lifetimes. Along these lines, in July 2005, the US Senate agreed on

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the need for mandatory carbon emissions caps and identified trading schemes as the most effective mechanism to accomplish these reductions. This vote reflects a turning point in the country, where the Congress shifted its focus from debating the existence of climate change to discussing the strategy to confront it. Indeed, senators of both parties have introduced legislation in the past that proposed cap-and-trade systems to limit CO2 emissions starting in 2010. Investors are also increasingly concerned about the physical and regulatory risks posed by climate change for fossil fuel–based power producers. This has been reflected in the increasing number of shareholder resolutions filed in the industry. During the 2005 proxy season, Dominion, DTE Energy, First Energy, and Progress Energy have received shareholder requests that a committee of independent board directors assess and report how management is responding to rising regulatory, competitive, and public pressure to significantly reduce CO2. All companies except Dominion have agreed to prepare climate risk reports. Dominion’s resolution is still pending. During the 2004 proxy season, six other electric power companies (American Electric Power, Cinergy, Reliant, Southern Company, TXU, and Xcel Energy) faced similar resolutions, and reportedly, all of them agreed to publish assessment reports. American Electric Power, Cinergy, Southern Company, and TXU have published reports to shareholders that outline the implications for the companies and their shareholders of climate change. American Electric Power did the most comprehensive job in analyzing future scenarios and quantifying their implications. These resolutions have prompted companies to review their policies and take a public stand on the climate change issue. Institutional investors continue to assess how climate policy issues may affect their portfolio construction, stock selection, and asset allocation. This trend is perhaps best exemplified by the formation of two groups of concerned institutional investors: the Carbon Disclosure Project and the Investor Network on Climate Risk. The former is a global coalition of more than 155 institutional investors with combined assets of $21 trillion; the latter comprises a dozen US state treasurers with more than $1 trillion in assets. Driven by concerns over potential hidden liabilities related to carbon emission, pension funds, trustees, assets managers, corporate directors, and executives as well as other fiduciaries will increasingly be held accountable for the prudent management of their portfolios with respect to climate risk. As a result, investors will increasingly consider companies’ individual carbon risk exposures and

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risk management capabilities as part of their fiduciary obligation, and therefore highly exposed companies will have an increasingly limited access to these funds. STATES TAKING THE LEAD IN THE USA In the absence of current federal action, states have taken the leadership in reducing CO2 emissions. The California Climate Action Registry, the Climate Trust in Oregon, and the Regional Greenhouse Gas Initiative (RGGI) are examples of state-level actions which increase regulatory risks or, at least, impose an administrative burden for energy companies, which will have to deal with changing rules in different jurisdictions. In 2005, California, the world’s sixth largest economy, committed to reduce its greenhouse gas emissions by 80% by 2050. Under the RGGI, nine northeastern states have developed a regional CO2 cap-and-trade system to stabilize emissions from 600 power plants starting in 2009 and then reduce them by 10% by 2020. Under RGGI, power plants will be able to invest in domestic offsets projects (i.e. projects that result in greenhouse gas emissions reductions, such as landfill gas sequestration, afforestation, natural gas/home heating, and end-use energy efficiency) to meet up to 50% of their reduction goals. They will also be able to invest in international offset projects under the Kyoto Protocol’s Clean Development Mechanism and in allowances (emissions permits) from the EU Emissions Trading Scheme. An increasing number of regulatory agencies are also beginning to calculate and consider the price of carbon in order to guide investments in the electric power industry in their states. The Wisconsin Public Service Commission uses a price of $15 per ton of CO2 as part of its evaluation of competing projects to generate electricity in the state. The California Public Utility Commission also requires investor-owned utilities to incorporate a “greenhouse gas adder” when evaluating competitive bids to supply energy. This suggests that US states are seeking to encourage electric utilities to internalize the cost of CO2 emissions. This will transform the cost hierarchy of fuels, as carbon-intensive technologies to generate electricity will become more expensive and other cleaner alternatives more competitive. An increasing number of states have taken legal action to promote legislation to address climate change, which increases the risk of litigation for fossil fuel–based power producers. Massachusetts, Maine, and Connecticut


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have filed a petition to compel the Environmental Protection Agency (EPA), under the Clean Air Act, to establish a national limit for CO2 emissions. Since then, 11 states and other entities have filed suits pursuing the same goal. Additionally, in July 2004, Connecticut, New York, California, Iowa, New Jersey, Rhode Island, Vermont, Wisconsin, and the City of New York filed a lawsuit against five power producers: Cinergy, American Electric Power, Southern, Tennessee Valley Authority, and Xcel Energy. These lawsuits allege that the CO2 emissions from the combustion of fossil fuels at electric generating stations contribute to global warming and amount to a public nuisance. Companies argue that CO2 reductions are not mandatory for their generating stations under current law. It is still unclear how these lawsuits will be resolved, which illustrates the increasing level of uncertainty that investors face in the electric utility sector. FOCUS ON ELECTRIC POWER COMPANIES Electric power companies continue to increase their environmental capital expenditures in order to meet stricter air emissions standards. Yet their ability to recoup such expenditures under restructuring and competition is increasingly uncertain. Historically, regulated electric utilities have had the privilege of fully recovering environmental capital expenditures and fuel costs with a return through regulated rates. However, restructuring continues to move the industry away from the historic monopolies, which increases competition and shifts the burden of any potential environmental compliance costs from consumers to investors. Specific restructuring plans differ across states, with most of them allowing utilities to recover stranded costs during transition periods and subject to regulatory approval. As a result, industry restructuring is fundamentally changing the way companies make money in this industry. In the US competitive wholesale markets, electricity prices are primarily driven by the marginal (variable) cost of the least efficient, or most costly, unit needed to meet the electricity load required by the regional market. Generally speaking, the most costly unit dispatched each day is a natural gas–fired peaker unit. In a CO2-constrained system, both coal- and gas-fueled power producers will see cost increases, although the impact on gas units will be less than that on coal units. As a result, electricity price increases in competitive markets will not be enough for coal producers to compensate for their carbon-driven higher costs and will thus result

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in lower profits. The availability of the gas supply will also play a role in this dynamic. A major uncertainty for investors in this industry is how long coal plants will be allowed to emit large quantities of pollutants, including CO 2, when cost-effective technology is available and public health concerns continue to grow. While the percentage of power generated from coal is projected to decline slightly, coal-fired plants will nevertheless remain as the main source of power. As a result, investors concentrated in companies with relatively large amounts of coal assets will demand higher returns for their increased exposure to hidden carbon liabilities. The current US administration still rejects the need for mandatory CO2 emissions goals. Yet power generators will have to address shareholder concerns and potential litigation when making their investment decisions. This highlights the great uncertainty faced by power plant generators, developers, and investors, who have to make long-term investment decisions, even in the absence of current carbon regulations. Unlike other assets, power plants have both long lead times for construction and long lifetimes of 20–50 years. As a result, tomorrow’s capacity is being designed and financed today. The US Energy Information Administration forecasts that the USA will build the equivalent of 1,350 medium-sized fossil energy power plants between now and 2025 (~405,000 MW), and therefore US emissions are projected to increase by 40% over 2000 levels if caps are not put in place. Unlike other air pollutants, CO2 stays in the atmosphere for hundreds of years. As a result, CO2 emissions of plants that are financed today will make the task of protecting the climate for future generations that much harder and more expensive. The greatest risk is that investors in this industry may face potential stranded costs as power plants financed today may have to be retired early in the future before having been fully depreciated, placing investors at risk. The same is true for capital expenditures in air emissions control equipment (or environmental upgrades) to address currently regulated pollutants (e.g. sulfur dioxide, nitrogen oxide, and mercury) that may become insufficient to reduce CO2 emissions in the future. The EPA estimates that electric utilities will spend about $50 billion on environmental upgrades, excluding CO2, through 2015, which represents approximately 12% of the sector’s market cap. Approximately 50% of the 1000 US coal plants are older than 30 years and will have to be replaced in the next 20 years. Given the need


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to replace this capacity and meet demand growth in a sustainable way, more energy-efficient and cleaner technologies need to be deployed. Currently, major fossil fuel power producers seek to continue relying on fossil fuels to meet higher electricity demand while seeking to reduce air emissions of their fleet. To achieve this, companies are extending the operating life of their old fossil assets through large capital expenditures. Although these capital expenditures in air pollution control equipment will substantially reduce air emissions of currently regulated pollutants (i.e. nitrous oxides, sulfur dioxide, and mercury), they do not address CO2 emissions, which remain as a great challenge. Investing in emissions control equipment at old plants or in new generating assets could be expensive if they subsequently prove to be inadequate to meet such impending future regulations as those to limit CO2 emissions. Regulations thatcurrently target nitrous oxides, sulfur dioxide, and mercury coupled with CO2 emissions cuts at a later date constitute the most expensive compliance scenario for most companies. The Stratus Consulting study by Repetto and Henderson (2002) confirmed that compliance costs with multi-pollutant caps, excluding CO2, are projected to be substantial, reaching at least 20% of the 2000 revenues for at least four companies in the electric power industry. AEP’s strategy, for example, is to continue using coal to meet higher electricity demand while reducing the air emissions. To achieve this, AEP plans environmental capital expenditures of $4.1 billion through 2010 (about $3.7 billion in environmental retrofits, mainly scrubbers that will reduce emissions of sulfur dioxide). AEP anticipates additional investments of $1.5 billion from 2010 to 2020 (more than two thirds of its current annual income) to meet the second-phase requirements of the Clean Air Interstate Rule and the Clean Air Mercury Rule,2 which target emissions of nitrous oxides, sulfur dioxide, and mercury, excluding emissions of CO2. These investments have the potential to materially affect the company’s financials and competitive position. Yet about two thirds of the total environmental capital expenditures projected by AEP through 2020 will address the air emissions of pollutants other than CO2. On the other hand, electric companies that take positive and proactive measures to address climate change can capture significant benefits for shareholders. Mounting evidence indicates that socially responsible investment leads to superior portfolio performance. Innovest3 found that electric power companies with above-average environmental management consistently outperformed their below-average industry peers financially

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during the last seven years. In 2005, for example, Innovest found that environmental leaders outperformed laggards by 1,700 basis points (or 17 percentage points) in total shareholder return (stock price appreciation plus dividends) over the over the past three years, from November 2003 to November 2005 (Figure 23.1). Environmental leaders, in this context, are the top-half group of companies with higher environmental ratings relative to the ratings of the bottom half group of companies. Innovest’s analysis comprises the environmental performance of the largest electric power companies included in the S&P 500. Carbon exposure and corporate strategic planning are part of this analysis. Firms with high Innovest ratings outperform largely because they tend to do a better job in managing financially relevant governance and environmental issues that are typically not addressed in conventional financial analysis. This is particularly true in this resource-intensive industry. Electric power companies that are better able to adapt to stricter

F I G U R E

23.1

Analysis of stock performance based on environmental ratings

Source: Innovest ratings, 2005, and Thomson financial data


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environmental concerns, including carbon emissions caps, will have a competitive advantage over their industry peers. Under the Innovest carbon practice, key criteria affecting the risk exposure of electric power producers include the regional distribution of assets, namely the proportion of these assets in markets with current or likely CO2 emissions constraints; prevailing power market dynamics, including the level of deregulation and regional marginal fuel; the legal and market ability of companies to pass compliance costs to consumers; the flexibility to diversify existing generation portfolios away from carbon-intense fuels; and the positioning to pursue and profit from emerging business opportunities in new less carbon-intense technologies. Equally important is the strength of corporate carbon governance, management systems, and mitigation strategies. As markets continue to understand and assess all these factors, financial impacts driven by climate change are increasingly expected to influence credit ratings, cost of capital, and insurance premiums. Electric power companies have adopted a wide range of management strategies to deal with the impact of impending carbon regimes. Leaders develop a public and formal carbon management strategy, formally allocate responsibility for managing carbon issues, follow third-party inventory protocols, commit to greenhouse gas reduction targets, and elaborate implementation programs to achieve them as well as monitor progress and follow third-party reporting protocols. These companies also conduct carbon sensitivity analysis, engage in emissions trading simulations, monetize external impacts of fossil fuel generation, and work with regulatory and industry bodies to develop proactive legislation. AEP, Consolidated Edison, Cinergy, Entergy, DTE, FPL, Exelon, Entergy, and Pinnacle West Capital have CO2 emissions reduction targets in place. The extent to which companies integrate environmental targets into their business strategies is a differentiator in this industry. Unfortunately for investors, virtually none of these differentials are being captured by traditional equity research. Innovest research indicates that electric power companies increasingly discuss climate change in their financial filings, but only a few companies provide emissions data, and until recently, none discussed their financial implications. Yet the Sarbanes–Oxley Act states that companies must disclose all material impacts, including non-traditional sources of risk. Sources of carbon-related data include investor presentations, corporate Web sites, and environmental and sustainability reports as well as responses to investor

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inquiries. Yet we found that the most relevant information in this area stems from regular interviews with senior management. Although current disclosure practices have improved during the last few years, reporting is far from standardized and varies widely within the industry, requiring substantial additional work for an in-depth analysis of climate risk. Some fossil power producers surprisingly state that climate change would have little effect on their businesses. The portfolio of mitigation options in this industry typically includes energy conservation and energy efficiency improvements, highly efficient combined heat and power systems, nuclear power, demand-side management, and fuel switching toward less CO2-intensive fuels as well as purchases and development of renewable technologies that range from biomass co-firing of coal plants to testing of hydrogen applications. Emerging opportunities to compensate CO2 emissions from power plants involve waste-to-energy projects, reclamation of SF6 gases, terrestrial carbon sequestration (i.e. forestation), and emissions trading (i.e. offset purchases) as well as exploration of carbon storage technologies. Key players in this industry anticipate that carbon emissions will ultimately become mandatory and agree that the uncertainty jeopardizes their ability to include carbon risks in today’s compliance decisions. An increasing number of companies recognize that potential compliance costs will most likely require vast capital expenditures, and therefore more openly support carbon emissions caps as they need certainty for effective business planning. FPL, Exelon, Reliant, Cinergy, and AEP, for example, are proactively working with regulatory and industry bodies on multipollutant cap and trade regimes. It is well established that a multi-pollutant approach and a trading regime similar to the Acid Rain Program would substantially lower overall compliance costs, removing a great deal of uncertainty for companies and investors. An increasing number of power generators integrate carbon constraints into their strategic planning. However, only recently, a few companies have formally introduced theoretical carbon prices into their decision-making process. As part of its resource planning process in Colorado, for example, Xcel Energy agreed with the Colorado Public Utility Commission to incorporate a proxy cost for carbon emissions (to account for potential future carbon regulation) and the value for renewable energy credits into the company’s evaluation of new power projects. Accordingly, Xcel Energy assumed a cost of $9 per ton of CO2 for a new power plant in Pueblo, Colorado. PSEG also incorporates the prices of


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emissions permits in modeling new and existing plants. This represents a major step, which would enable companies to better allocate capital and make strategic business decisions in light of climate change. A portfolio of cleaner and more energy-efficient generating technologies in tandem with energy efficiency improvements is being pursued by power producers to limit their carbon intensity. Increased efficiency in generation, transmission, and end use of electricity and continued use of coal with the potential of developing carbon capture and sequestration as well as expansion of renewable power and nuclear generation will contribute to this goal. Yet cost, reliability, safety, and siting as well as market and public acceptance are among the challenges still ahead. Currently, most electricity is produced in conventional coal steam boilers with average efficiencies of 30–42%. This means that less than a third of the energy input, on average, is converted into electricity, and the rest is wasted. Along these lines, the World Energy Council (2004) concludes that up to $80 billion in investment in new capacity each year can be saved simply by improving the efficiency and best practices at existing power plants worldwide. Coal-based producers continue to invest in advanced coal technologies, seeking to keep using their coal assets and resource supply under stricter air emissions scenarios. Pressurized fluidized bed combustion, an example of these advanced coal technologies, runs under supercritical levels of temperature and pressure, increasing efficiency rates from 38% of the traditional pulverized coal plants to up to 45%. With today’s technology (i.e. using an amine scrubber on the flue gas), it is estimated that a coal plant with a net output of 500 MW would have to consume 31% more coal just to run the CO2 scrubber and that the cost of post-combustion carbon capture in a new pulverized coal plant would be about $50 per metric ton of CO2 (Rubin et al., 2004). Carbon capture and sequestration is likely to be far more cost-effective in combination with advanced coal technologies, such as integrated gasification combined-cycle technology. The potential of carbon emissions restrictions impacts the choice of technology when building the new capacity. On a Btu basis, the combustion of coal produces more CO2 than the combustion of natural gas or of most petroleum products. Carbon dioxide emissions per unit of energy obtained from coal are approximately 20% higher than those from residual fuel oil, which is the petroleum product most widely used for

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electricity generation, and about 80% higher than those from natural gas (Energy Information Administration, 2001). Switching from coal-based to gas-based production will reduce, ceteris paribus, carbon emissions by 60%. Combined-cycle gas turbine (CCGT) plants, for example, are less carbon-intense (0.3 ton per MWh) than coal (0.9 tons per MWh). CCGT plants also have the lowest capital costs but the highest variable costs; fuel costs represent about two thirds of total costs. As a result, CCGT plants are very sensitive to changes in natural gas prices. On the contrary, coal plants have relatively higher capital costs, but fuel-variable costs account for a smaller percentage of total costs. Expansion of the US access to liquefied natural gas would substantially improve the likelihood that natural gas displaces coal-fired generation in the future. Despite the high natural gas prices, CCGTs hold an advantageous position as the cleanest and most efficient of the fossil fuel–based technologies. Modern natural gas–fired combined-cycle facilities produce 90% less air emissions and are about 40% more fuel-efficient than the average US fossil fuel power plant. CCGT power plants have 55% efficiency rates. Southern, which is among the top CO2 emitters in the US, continues to install new gas combined-cycle capacity, planning to reduce the share of coal fuel to 62% by 2010. The goal of Southern’s management is to reduce the company’s exposure to air emissions pressures as it increases capacity to meet higher electricity demand. IGCC HOLDS PROMISE Integrated coal gasification combined-cycle technology (IGCC) is a technology that is increasingly regarded as clean coal (i.e. higher efficiency rates and lower cost to capture carbon emissions relative to pulverized coal) that may allow companies to use coal assets under carbon-constrained scenarios. Relative to pulverized coal, IGCC capital investments are between 10% and 20% higher (Rubin et al., 2004), but efficiency and environmental benefits are also substantially higher. Efficiency rates of IGCC plants can reach 46–50% (dependent on coal quality), compared to 37% of pulverized coal plants and 45% of supercritical coal plants. According to experts, carbon capture and sequestration are likely to be far more cost-effective in combination with advanced coal technologies such as IGCC technology. IGCC allows for the pre-combustion separation


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of carbon, resulting in a concentrated stream of CO2 and gaseous hydrogen that can be easily separated. The hydrogen then provides a clean, carbon-free fuel for combustion,4 and the CO2 is sequestered by injecting it deep underground. The gasification process also facilitates the separation and capture of conventional pollutants and impurities, such as sulfur and mercury. The 2005 Energy Policy Act provides substantial support to develop this technology in the US. The act authorizes $800 million in investment tax credits for IGCC projects. Qualified projects are those that power new electric generation units or retrofit or repower existing plants (including existing natural gas–fired combined-cycle units) that use coal for at least 75% of their fuel input and have a total nameplate generating capacity of at least 400 MW. Under the Clean Coal Power Initiative, the legislation also provides $1.26 billion during 2006–2014 to fund IGCC projects that meet certain emissions reduction and thermal efficiency goals. AEP plans to build a $1 billion, 600 MW IGCC facility in Indiana. Southern Company plans to build a 285 MW thermal coal gasification plant in central Florida with Department of Energy (DOE) co-funding of $1 billion. Cinergy is also considering building an IGCC plant in Indiana, reporting that it has completed a feasibility study with GE and is in the process of filing a regulatory case for full cost recovery. AEP, for example, reports that for a 600 MW plant, the all-in cost of electricity (without CO2 capture) is $47 per MWh for the pulverized coal and $50 per MWh for the IGCC. When investments for carbon capture are considered, the estimated all-in cost of electricity is $76 per MWh for the pulverized coal and $70 per MWh for the IGCC (American Electric Power, 2004). Transporting and injecting the captured carbon in a geologic repository would add further costs. It appears from preliminary studies that the costs of transport and injection will be approximately 10% of total carbon capture costs for IGCC plants (Rubin et al., 2004). Currently, IGCC technology exists at a commercial scale, primarily in the chemical industry, and the technology for injecting CO2 underground is utilized today to enhance oil and gas recovery operations. There are 160 commercial IGCC plants operating, under construction, or planned in 28 countries. While electric power companies are engaging in this technology, oil companies are also expected to become major players in the industry. Among the barriers to applying this technology are current price levels, a technology risk with no operating history, and the need for design changes to assure adaptability to carbon constraints.

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Investment opportunities in renewable power technologies are a critical component of the strategic response to climate change. Today’s skyrocketing oil prices, combined with cost reductions and technological breakthroughs, converge to increase interest in renewable power. Advantages of renewable power (e.g. wind, hydro, photovoltaic, biomass) are by now well recognized: declining cost (in certain situations), modularity, flexibility, lack of need for large capital investments, lack of reliance on volatile fuel prices, and, of course, low environmental impact. The International Energy Agency projects that renewable power will capture a third of total investments in new generating assets through 2030. Royal Dutch/Shell has predicted that renewables will account for 15% of all OECD energy production by 2020, the point at which the World Energy Council estimates that the global renewables market will reach $1.9 trillion. Wind power, for example, has become price-competitive with gas in many parts of the US. GE’s wind turbine business has grown dramatically over the last few years and generated about $2 billion in sales in 2005. The photovoltaic market, currently worth $7 billion per year and with annual growth of 35%, is no longer marginal. Sales of solar panels amounted to $11 billion in 2005, up 57% from the previous year (Euromoney, 2005). The 2005 Energy Act provides tax credits of up to $2,000 on new solar panels. Companies that engage in renewable power and distributed generation technologies will benefit from gaining expertise and strategic positioning in a niche market, lowering exposure to fluctuating fossil fuel prices, protecting themselves from grid disruptions, and reducing operating costs due to avoided air emissions charges as well as creating additional assets from environmental benefits that they can monetize in the emerging tradable emissions permit markets. In the USA, for example, FPL Group has successfully positioned itself as the leader in developing renewable sources of energy. Its subsidiary, FPL Energy, has an industry-leading position in wind energy with approximately 40% of the current US installed wind capacity and also became the largest generator of solar power in the country in 2005. Renewable power is a key component of its business strategy, allowing the company to fulfill its social responsibility goals while being instrumental in meeting the increasing demand for electricity in its operating areas as well as yielding positive financial returns. Senior management emphasizes that while the renewal of federal support (i.e. PTC) is a key component of wind project financing, wind power is close to becoming competitive on its own.


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DTE is also well positioned to pursue profitable business opportunities driven by environmental factors. The $100 million portfolio of its subsidiary, DTE Energy Ventures, is active in distributed generation, fuel cell technologies, and hydrogen technologies. Its renewable hydrogen system, which was built under a DOE $3 million award, started operations in 2004, providing up to 500 KWh of carbon-free hydrogen daily for the fueling of up to five hydrogen fuel cell vehicles. The company currently works with automobile companies on marketing of these services to promote demand. Wall Street is starting to embrace opportunities in this space. For example, CSFB has arranged $1.8 billion in financing for various renewable energy projects in the last two years. Pension funds are also active. The Clean Energy States Alliance, including 12 US states, plans to invest $1 billion in renewable power. Nearly all companies in this sector endorse emissions trading schemes and credit for early action to reduce CO2 emissions. A price that fully reflects the cost of delivering energy is the most effective driver of efficiency for both energy conversion (power plants) and end-use purposes (consumption). However, not all costs are currently reflected in market prices. This is particularly true of long-term investments in infrastructure and environmental impacts that are generally distant in time and geography. Carbon trading schemes are designed to provide companies and investors with information to make informed investment choices. The emergence of carbon as a new traded asset class illustrates the potential for new business driven by carbon emissions management and regulatory schemes to tackle climate change worldwide. Under the EU’s scheme, about 2.2 billion carbon emission allowances are issued for each of the three years to 2007, each equating to one tonne of CO2. The emissions market is worth about E44.0 billion per year ($55.4 billion), at September 2005 prices. Morgan Stanley assumes that the European carbon trading markets will develop into a large liquid market with interest from small and large compliers as well as financial brokers and speculators. The markets for futures and derivatives are expected to develop also for hedging and profit purposes. Current estimates of total volume traded are at about 5 billion tonnes in 2008 and 8.4 billion tonnes in 2010. In the EU, the net shortage for emissions allowances and the high marginal cost of CO2 reduction indicate that carbon prices will continue to increase. Estimates suggest that the EU emission trading scheme is net short by about 130–200 million tonnes in Phase I (2005–2007) and 2.7 billion tonnes during the second phase (2008–2012). Morgan Stanley

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projects medium-term sustainable carbon prices of E15/ton. This is below the level forecasted to persuade utilities to switch to less carbon-intensive fuels. Dresdner Kleinwort Wasserstein and RWE, the German utility, have stated that the cost of allowances would need to rise to between E20 ($25.20) and E30 ($37.80) per tonne to encourage fuel switching. This market price signal is expected to spur private sector innovation and investment while also deterring carbon-intensive fuel investments. OUTLOOK FOR US CARBON REGIME Although the cap-and-trade program under the international Kyoto Agreement was proposed by the USA, it has not ratified the agreement, and American companies will not be able to participate. In the USA, there is no mandatory carbon scheme at a federal level. Yet companies eager to gain carbon-trading experience have gathered to found a voluntary and self-regulated trading exchange, the Chicago Climate Exchange. The scheme’s goal is to demonstrate the viability of a multi-sector greenhouse gas emissions cap-and-trade program in the USA. The development of a liquid US market is still uncertain, but state action and corporate demand for regulations are driving expectations. Climate change is the greatest challenge facing electric power companies since they have to make long-term capital investment under changing industry rules and a still uncertain carbon regulatory regime. Increasing competition and ongoing restructuring exacerbate the potential financial impact of impending carbon emissions caps. Driven by concerns over potential hidden carbon liabilities, shareholders, investors, regulators, and consumers will increasingly hold corporate executives and directors accountable for their cautious climate change management. The implications of climate change are expected to materially impact corporate returns and shareholder value both through increased capital expenditures and emerging profit opportunities. Even if they are not convinced that human-made CO2 emissions are causing climate change, managers of fossil fuel-based power companies need to assess the potential implications of climate change in light of the increased investor risks. As investors increasingly consider companies’ individual carbon risk exposures and management capabilities as part of their fiduciary obligation, major CO2 emitters will face higher debt charges from conscious lenders, increasing insurance premiums, and a limited access to both mainstream and socially responsible funds.


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REFERENCES American Electric Power (2004) An Assessment of AEP’s Actions to Mitigate the Economic Impacts of Emissions Policies (August 31). Available at www.aep.com/ environmental/performance/emissionsassessment/default.htm. Energy Information Administration (2001) Emissions of Greenhouse Gases in the United States 2000, DOE/EIA-0573 (November), Table B1 (Washington, DC: US Government Printing Office). Euromoney (2005) International Power & Utilities Finance Review, 2005/2006 (London: Euromoney Books). Repetto, R and Henderson, J (2002) Environmental exposures, transparency, and strategic management in the U.S. electric utility industry. Unpublished paper, Stratus Consulting. Rubin, ES, Rao, AB, and Chen, C (2004) Comparative assessments of fossil fuel power plants with CO2 capture and storage. In Proceedings of 7th International Conference on Greenhouse Gas Control Technologies. Available at uregina.ca/ghgt7/PDF/ papers/pcc-/475.pdf. World Energy Council (2004) Performance of Generating Plant: New Realities, New Needs.

NOTES 1. Professor Kerry of the Massachusetts Institute of Technology found that the total annual destructive potential of tropical storms in the North Atlantic and Pacific basins has doubled during the past 30 years. The energy driving hurricanes comes from warm sea surface temperatures, which increase in line with global temperatures. 2. For the full report, refer to www.aep.com/environmental/performance/emissionsassessment/default.htm. 3. Innovest is a financial services firm analyzing non-traditional drivers of investment risk and value. 4. Alternatively, the hydrogen can be used for distributed generation applications or as a fuel for motor vehicles.


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Green, White, and Red Certificates Trading in Italy Stefano Alaimo

INTRODUCTION The Kyoto Protocol represents a fundamental moment in the engagement of the international community to combat atmospheric pollution in favor of sustainable development. Human activities have contributed greatly to the increase in the level of carbon dioxide in the air in recent decades. A study of the UN Secretariat on Climate Change revealed that 96.7% of greenhouse gas (GHG) emissions come from fuel combustion, and the energy industry sector is responsible for 39.1% of this amount of emissions. The Kyoto Protocol listed some initiatives that can be taken to reduce the GHG emissions in the energy sectors. These measures include promotion of electricity production from renewable sources, increased energy efficiency, and limitation of industrial installations’ emissions. These measures can directly affect energy production and consumption as well as energy-intensive industrial activities. In an effort to reach the Kyoto Protocol target of a reduction of 8% of GHG emissions by 2008–2012, compared to 1990 levels, Europe is concentrating its efforts on those policies, having approved a directive on the promotion of electricity production from renewable sources, a proposed directive on the promotion of end-use efficiency, and a directive (2003/87/CE) that introduces an emissions trading system (ETS) among all member states. In general, there are several tools for achieving emissions reductions, such as state subsidies, fiscal incentives, and market mechanisms. Compared 355

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to the other measures, market mechanisms are believed to be able to lower the overall costs of achieving emissions reductions and to favor use of the most energy-efficient technologies. In Europe, and around the world, many countries have adopted a market mechanism to promote environmental policies. Green certificates have been introduced in some countries (UK and Italy foremost among them) to promote renewable sources in electricity production, while white certificates have been introduced in Italy (and will become operational in France during 2006) to promote energy efficiency. Finally, the EU ETS will allow an efficient limitation of industrial plants’ emissions through red certificates trading. GREEN TRADING To encourage the production of electricity from renewable sources, several policy instruments can be used. Generally, they can be divided into support mechanisms and market mechanisms. Within the support mechanisms, feed-in tariffs are perceived as very successful tools for stimulating investment in renewable energy production. These guarantee remunerative long-term prices for producers but are probably too costly because they are not efficient. Many countries decided to introduce a market mechanism based on a quota target and tradable certificates (TRECs). In Europe, the Netherlands, Belgium, the UK, and Italy developed different TRECs systems as a part of their renewable energy policies. A quota system consists of an obligation to produce1 a percentage of the total annual electricity production as “green” electricity from renewable sources. The green electricity is eligible for green certification, which is released by an issuing body (usually the national transmission system operator). Electricity producers, who are the obligated parties, can decide whether to comply through electricity production from their own plants or to buy green certificates from someone else and redeem them to meet the target. Tradability gives green certificates an economic value that can be transferred from one party to another. The final effect is to stimulate most efficient technologies that can earn more from that mechanism and to lower the total cost for obligated parties. In the UK, for example, the renewable electricity produced within the country is rewarded with renewable obligations certificates (ROCs). Producers must produce 3% of the total annual production as renewable, and they may meet the obligation by redeeming an equivalent amount of ROCs. ROCs are tradable bilaterally and can be used to meet the producers’ target.

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Those who cannot meet the target and do not buy ROCs can buy out the obligation at a price of £30 per megawatt-hour. The money collected from companies that do not comply with the obligation and pay the penalty is distributed among the companies that have met the obligation. This redistribution will be done in proportion to the number of ROCs which made it possible for the market prices to go above the penalty level. As far as Italy is concerned, a feed-in tariff system was adopted in 1992 to promote electricity production from renewable sources (known as the CIP6 mechanism). In 2002, that system was replaced by a quota obligation introduced for producers and importers that must inject into the grid 2% of their production of the previous year from conventional sources. The 2% quota has been increased by 0.35% each year for the period 2004–2006. This obligation can also be satisfied by delivering an equivalent number of “green certificates” bought from those who produced electricity from renewable sources and received a certification by the Italian transmission system operator, GRTN. To facilitate trading of green certificates in 2003, the Italian Power Exchange (GME) was tasked by law to organize an electronic platform where operators can buy or sell certificates in continuous trading sessions. A real-time link with the registry of the exchange where green certificates are tabulated can avoid the risk of double selling by providing a guaranteed supply of certificates as buyers must deposit these certificates before trading sessions begin. This gives the market security for payments. Prices are then likely to be efficient, and producers can receive a “fair” remuneration of their investment selling certificates. Prices registered in the GME platform have always been above E80/MWh, hitting E97/MWh for 2004. Those prices represent only the remuneration for producing electricity for renewable sources. Total revenues for producers are equal to that component plus the income for the electricity sold into the market. The introduction of the green certificates market mechanism meant a unique price for producers from renewables, independent of the specific source used to produce the electricity. The effect was to create a strong incentive to build new plants using the cheapest technologies. Most expensive technologies are not properly supported with the actual mechanism, even though there are some proposals to extend the period of certification for those technologies. (For example, there is a proposal to extend the period of certification to 12 years for production from biomass. All other technologies have the right to receive certification for eight years.)

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WHITE TRADING Energy efficiency is another field where market mechanisms can be applied. To promote energy efficiency and energy consumption reduction, a quota target can be assigned to parties that can achieve the goal by implementing projects and measures that increase final use energy efficiency and save energy. For each unit of energy saved, a white certificate is released and can be traded among operators. Obligated parties will have two options: implement their own measures to reach the target or buy white certificates and redeem them for obligation purposes. Non-obligated parties can participate through this mechanism for efficiency projects by receiving white certificates for the energy saved through them and selling those certificates to obliged parties. An example of how this system works is seen in France. The French recently introduced white certificates as a tool to meet targets for energy savings. The scheme assigns an obligation to save energy to energy distributors (electricity, gas, LPG, domestic fuel not for transportation, cooling and heating for stationary applications). That obligation can be satisfied by implementing energy-saving measures or by buying white certificates. Obligated parties can also pay a penalty to fulfill the target. The total energy-saving target for France is 54 TWh to be saved during the period 2006–2008. That amount is to be shared among all the distributors under obligation. The calculation of the individual targets for year t is made considering the total amount of energy sold during year t – 2. The obligation can be satisfied at any moment within the 2006–2008 period. White certificates are released to those who implement eligible energy-saving actions. The amount of energy saved, or number of white certificates released, can depend on type of equipment or goods, the process used to save energy, and eventually, geographical area. An eligible body must save at least 3 GWh, with the possibility that parties can collaborate to reach the target. Actions must be additional to the “business as usual” activities, and the certificates are delivered after the programs are carried out, but before the energy saving takes place. A white certificate can be used for 10 years for fulfilling a target. Italy was the first country to introduce a white certificate system, with 2005 being the first year of obligation for Italian electricity and gas distributors (i.e. those with more than 100,000 customers connected to their grids on December 31, 2001) to achieve a target of energy savings. The scheme gives distributors a five-year period of obligation (2005–2009),


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with national targets increasing exponentially through the years, as shown in Table 24.1. Those targets are then shared among distributors according to the consumption of their customers compared to the national figures. The energy savings can be achieved through energy efficiency projects implemented by distributors themselves. The projects can also be undertaken by energy service companies (ESCOs). GME is responsible for organizing and managing the white certificates registry, where each distributor and ESCO will have an account in which certificates issued or bought are deposited and is also responsible for creating and managing an organized market. For each tonne of petroleum equivalent saved, the energy and gas authority certifies the amount of saving achieved through a project. After authority certification, GME can issue energy efficiency certificates (white certificates) to the project maker. The white certificates can be traded between owners and buyers, both bilaterally and in the GME market. ESCOs will be interested in implementing projects and selling the white certificates. On the other side of the transaction, energy distributors are net buyers in the market since they are under obligation to buy certificates. Distributors can comply with the obligation by either implementing their own projects and getting the equivalent number of certificates or buying white certificates through the market. The choice is made by comparing the marginal cost of implementing an efficiency measure with the certificates’ market price. Certificates’ tradability means that the total cost for achieving the global national target is minimized. By April 30 of each year of the program, starting in 2006, the parties under obligation must redeem a number of white certificates related to their target. For those who do not comply, sanctions are applicable. T A B L E

24.1

Italian energy saving targets, 2005–2009 (Mtep) Year

Electricity

Gas

2005 2006 2007 2008 2009

0.1 0.2 0.4 0.8 1.6

0.1 0.2 0.4 0.7 1.3

Source: GME, Italy.

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RED TRADING European Directive 2003/87/CE introduced an ETS in Europe on January 1, 2005. Industrial plants, which operate within Annex I of the Directive, will have the obligation to deliver an amount of emission unit allowances (EUAs) equal to their emissions released into the atmosphere each year. The deadline for complying with the obligation is April 30 of the year after the emissions were created, starting in 2006. The Directive set up two periods of obligation (2005–2007 and 2008–2012) through the end of the Kyoto Protocol in 2012. Each member state has to present a national allocation plan (NAP) with the total number of EUAs to be allocated to the installations operating in the activities under obligation. The total number of allowances to be allocated during each period must be approved by the European Commission. The NAP must also determine how the allowances are distributed among the installations. The European Commission does not verify this process, provided that the allocation is free for at least 90% of the total number in the first period of obligation (and at least 95% for the second period of obligation). Also, the total number allocated to each installation is a national decision. Once they have the NAPs, the installations will receive EUAs at the beginning of each year and will surrender them 12 months later, delivering an equal number equivalent to their emissions. It is very likely that the number of EUAs allocated to one installation will not be exactly the number it needs to comply with the obligation. The Directive allows EUAs to be traded among installations: each plant can buy or sell EUAs with another plant based in any of the EU member states. To facilitate EUA transfers, each state must organize a registry in which all the allowances are deposited in accounts. Each obligated installation will have its own account with the EUAs deposited and can access the registry at any time to check its portfolio position. When a trade is done, the seller submits the trade to the national registry, showing the number of allowances traded and the buyer. A real-time link among all the European registries will allow an immediate transfer from the seller’s account to the buyer’s account. Even though there was some delay in the approval process by the European Commission because of the over-generous NAPs proposed by some countries, trading of allowances started at the beginning of 2005. The preferred way to trade EUAs was through over-the-counter brokers, negotiating forward bilateral contracts with delivery in December 2005. From April 2005, some European power exchanges


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organized market platforms to trade EUAs, but roughly 70% of the EUAs were traded through bilateral contracts by August 2005. Many operators are forecasting an increase in the percentage of allowances traded in organized markets, and in 2006 the total number of EUAs traded through the organized markets will probably be higher the number traded by over-the-counter brokers. It is very interesting to look at the price behavior of EUAs since trading began. At the beginning of 2005, the EUA market price was around E8 per tonne as operators were convinced that the market was over-supplied, especially looking at the NAPs proposed to the European Commission. Each member state was trying to propose a very generous allocation for their national plants in the attempt to give them a number of allowances for free that could be traded in a more competitive position compared to the same plant in another country. Step by step, the NAPs finally approved by the Commission became somewhat more restrictive than the member states proposed, and market operators developed the idea that the supply was not big enough. At the same time during 2005, the world experienced a rapid increase in the crude oil price, which caused a corresponding increase in the natural gas price, one of the fuels most used to produce electricity. In this situation, because the natural gas/carbon prices spread widened, the market discounted the fact that many electricity producers would decide to use more gas than more carbon-intensive fuels, such as coal or oil, to produce electricity. In so doing, the expectation was that there could be more demand for EUAs as more carbon-intensive fuels are more polluting than natural gas. The effect was an upward pressure on EUA prices that led to operators trading contracts with December 2005 delivery at almost E30 per tonne of carbon dioxide in mid-2005. The delay in the approval process between national governments and the European Commission for the total amount of allowances to be allocated to each member state for the all NAPs and the delay in the full operation of the national registries contributed to limiting the number of sellers in the market and maintained an upward pressure on prices. Last but not least, market operators wrongly believed that new EU entrants could contribute an increase in the number of EUAs offered. While delays in organizing national registries made it impossible to accept offers for a time, the high economic growth rates changed the needs of those countries that might be forced to cover more emissions than everybody thought. All those factors are believed to be responsible for an EUA price increase in excess of 300% compared to early 2005 levels, as shown in Figure 24.1.

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F I G U R E

24.1

EUA price, E/tonne (August 2003 to August 2005)

Source: www.pointcarbon.com

CONCLUSIONS Market mechanisms are perceived to be the most efficient way to reach environmental targets at a minimum cost. Tradable certificates are used to transfer value from those who need to reach a target to those who are more competitive in implementing measures. The engagement of governments to combat climate change has created several country-specific markets where certificates usually cannot be used in a country different from the one in which they were issued. The next challenge is to create a global market where certificates are “fungible” and attractive for financial investors, who can enter into the environmental markets and create the liquidity that can really give a boost to investments in favor of sustainability and make the battle against climate change a success. NOTE 1. Such obligation can alternatively be placed on consumers.

C H A P T E R

25

Carbon Trading: A New Commodity Is Born Ashutosh Shastri

INTRODUCTION The implementation of the Kyoto Protocol, following considerable international diplomatic manouevering which led to Russia ratifying the Protocol on February 16, 2005, has heralded the birth of a new energy marketrelated commodity: carbon dioxide (CO2), henceforth referred to in this chapter as “carbon.” We have entered a new era in energy market dynamics. This chapter provides an overview of the various policy mechanisms and instruments that have led to an emergence of a traded market for carbon. It also discusses the observed trends and provides an analysis of the policy drivers. It concludes with a look forward to carbon pricing, laying out possible paths to the development of a global market for carbon. It is our view that the emergence of a value on carbon will be a key driver for energy investments for decades to come. This fundamentally alters the evolution of energy policy technology developments as well as the way the investment community views its future risk exposure. Previously, energy policy has concerned itself with managing and optimizing three key variables: market-based or market-reflective pricing (highly quantitative); security of supply (part quantitative and part political); and reliability and diversity of supply (more political than quantitative). However, with the signing of the Kyoto Protocol, a fourth variable 363

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has firmly established itself in energy policy-making: environmental compliance—this is no longer optional, but required. Just when policy makers were beginning to become comfortable with optimizing the scope of the solution space within the three variables, environmental compliance as a fourth variable emerged. Short- to medium-term energy policy makers will have to rebalance their equations to solve for environmental compliance. This will not be easy. Energy markets will be increasingly policy-driven, posing greater risk for investors. The energy sector players will make sharp strategic choices. Emergence of wholesale electricity markets with capacity payment incentive mechanisms will induce utilities to continue to operate their existing, already depreciated coal-fired plants. A carbon constraint regime will force them to decommission their coal portfolio and invest in cleaner, non-fossil generation assets. Carbon pricing, a key consideration in environmental compliance, is set to become an unavoidable risk in energy pricing and energy policy-making. THE GENESIS OF CARBON The emergence of carbon pricing has a long international political history. The genesis of the protocol early in 2005 goes all the way back to the first international agreement on climate change in 1990. The UN approved the start of climate change negotiations. The policy framework was initially crafted in the United Nations Framework Convention on Climate Change (UNFCCC) at the Earth Summit in Rio de Janeiro in December 1992. Subsequent international conferences of parties to the UNFCCC and meetings of parties (known in climate speak as “CoP and MoP processes”) advanced the international negotiations. At the third conference of parties in Japan the parties to UNFCCC signed the Kyoto Protocol. At this meeting a minimum of 5% reduction from 1990 levels during the period 2008–2012 was agreed. The EU participated in these meetings as a single political bloc. As part of this process, the EU signed a “burden-sharing agreement” which committed it to reduce carbon emission levels by 8% from 1990 levels. Ever since the negotiations began in earnest in 1992, the latest consistent international data for carbon emissions available have been for 1990. This is how the year 1990 has formed the basis for quantification in the Kyoto Protocol process. Figure 25.1 provides an overview of the climate negotiations processes. There has been considerable debate about the future of the

CHAPTER 25

Carbon Trading: A New Commodity Is Born

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25.1

Evolution of the Kyoto Protocol

Source: Kyoto Protocol documents; IPCC; EnerStrat analysis

Kyoto Protocol beyond 2012 as real business implications are becoming apparent, particularly within the EU. The US’s open opposition added further complexity around the long-term continuance of the Protocol. It appears inconceivable now, after Katrina and other energy-climate events, that the USA will simply ignore climate change and its associated risks. Whether Kyoto is the global instrument of choice or whether indeed a more regionspecific or theme-based pan-regional scheme emerges is an evolving debate. Later in this chapter we examine the likely future paths climate markets may take. It is increasingly clear that the USA simply cannot afford to ignore the issues and risks related to climate change or the emerging climate markets. We believe that the Kyoto process will continue beyond 2012. The market design may be considerably different. The international policy angle is articulated and forms the final section of this chapter. Our view is that whatever form the next generation of global climate markets takes, the European Union Emissions Trading Scheme (ETS) will significantly drive their shape, structure, and design. The EU ETS is the first regional carbon market-making mechanism. The lessons learned from its

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experience, 18 months so far, are covered and analyzed in this chapter and provide valuable insights into the post-2012 environment. International carbon markets are now a reality. The energy sector simply cannot ignore carbon anymore, nor can any sector where energy forms a dominant part of the cost structure. The E31/tonne price reached in the EU ETS, its subsequent temporary crash to around E9/tonne, and its rebound to around E16/tonne within less than a week have forced the European energy sector and the global investment community to focus on carbon pricing. THE POLICY FRAMEWORK The policy framework surrounding climate change flows from the initial agreement of the UNFCCC, in 1992, to the Kyoto Protocol and its various flexibility mechanisms. These flexibility mechanisms are at the core of the Protocol and seek to deliver the least cost option for achieving emissions reductions. The United Nations Framework Convention on Climate Change The first significant international agreement on climate change coalesced in December 1990 when the UN approved the start of treaty negotiations. In December 1992 the UNFCCC agreement was signed by 166 countries attending the Earth Summit in Rio de Janeiro. Thirty-six Annex I parties to the UNFCCC, which were either industrialized countries (being members of the Organisation for Economic Co-operation and Development) or countries in transition to a market economy, agreed under Article 2 of the UNFCCC to reduce emissions of six greenhouse gases (GHGs) to safe levels. Implementation of Article 2 was discussed at subsequent meetings of the CoP. CoP and MoP At CoP3 in Kyoto in December 1997, the Parties to the UNFCCC signed the Kyoto Protocol. This provided a mechanism by which Article 2 of the UNFCCC could be implemented. In Annex B of the Kyoto Protocol, which is a list of countries classified as Annex I parties to the UNFCCC, 39 parties (countries) undertook legally binding obligations under Article 3 of the Protocol to reduce GHG emissions by an average of 5% compared to 1990 levels during the 2008–2012 period. Throughout the remainder of

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this chapter we refer to developed countries which have accepted legally binding targets and are signatory to the Kyoto Protocol as Annex B countries (e.g. Germany, the UK, Australia) and developing countries which may have ratified the Protocol or are in the process of moving legislation for ratification but have not accepted any binding targets as non-Annex B countries (e.g. China, India, Brazil). We recognize the large opportunity for confusion in the Protocol documentation, as parties to the protocol have been referred to as Annex I (before the ratification and coming into force of Kyoto), Annex B, or non-Annex B, etc. (after ratification). THE KYOTO PROTOCOL Discussions began for GHG programs in 1992. The last available set of consistent international data are for 1990. This is the reason why 1990 forms the reference year for all baseline calculations. At the meeting of Annex B countries, a minimum floor of 5% reductions from 1990 levels by 2010 was set. The key countries that took on targets are shown in Figure 25.2. The EU, negotiating as a single economic bloc, F I G U R E

25.2

Key content of Kyoto Agreement

Source: UNFCC Papers; Renewable Energy Journal; EnerStrat analysis

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was expected to demonstrate leadership due to its inherent flexibility between member states. It did accept the highest reduction of 8% from 1990 levels. Subsequently, the USA pulled out of the Kyoto process, and so far, its public stance is against Kyoto ratification. Figure 25.3 describes the three key flexibility mechanisms, which are built into the Kyoto regime and are discussed in detail in the following section. It is important to bear in mind how these seemingly small numbers of 5% or 8% are non-trivial targets. Assuming a business-as-usual (BAU) path in growth in expected emissions, the actual reduction target for all Kyoto compliance entities is somewhere in the 16–18% range from 1990 levels. (See Figure 25.2, which describes the reduction target for the EU.) Following the ratification of the Kyoto Protocol by Russia in January 2005, it became an enforceable, legally binding international climate accord. Signatory member states are now legally bound to demonstrate emission reductions. The compliance period of the protocol runs from January 1, 2008, until December 31, 2012. The responsibility of ensuring Kyoto regime continuity by making amendments to the design of the compliance mechanisms, if required, is with the joint CoP/MoP committees. F I G U R E

25.3

“Flexibility mechanisms” in the Kyoto Protocol

Source: UNFCC Papers; industry journals; EnerStrat analysis


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Since the Kyoto Protocol entered into legal force on February 16, 2005, the CoP to the UNFCCC, serving as the MoP to the Protocol, has begun to make decisions relating to the Kyoto Protocol and its flexibility mechanisms, the Clean Development Mechanism (CDM), Joint Implementation (JI) and emissions trading, also referred to as “Assigned Amount Unit” (AAU) trading. Canada hosted the first joint CoP/MoP since the ratification of Kyoto in Montreal from November 28 to December 9, 2005. A landmark agreement extending the period of the protocol beyond 2012 has been achieved. The subsequent meetings of the CoP/MoP are expected to develop through extensive international negotiations. We expect announcements of the extension of the compliance period beyond 2012 to be announced some time in 2007–2008. The Kyoto Protocol has been ratified by 119 countries, but these countries have not accepted legally binding targets. These are known as non-Annex B countries and constitute the vast majority of the developing world, including China, India, Brazil, Chile, and Argentina. FLEXIBILITY MECHANISMS The Kyoto Protocol offers Annex B countries the choice of three flexibility mechanisms to reduce GHG emissions in the most cost-effective and efficient manner. The flexibility mechanisms offer a wider variety of instruments than a simple carbon tax and ensure that emission abatement is facilitated by a north–south dialogue. One concern raised by the financial community relates to investment incentives for clean energy projects. Considering the volatility seen in market-based mechanisms such as the EU ETS, carbon taxes are beginning to be viewed as “acceptable” from the point of guaranteeing investments. It is unclear whether the carbon tax regime will gain acceptance in the compliance period beyond 2012. It was envisioned that “flexibility mechanisms” provide an opportunity for players to manage their compliance targets in the most cost-effective manner and provide an opportunity for developing countries to acquire sustainable energy resources. There is no single global market for carbon, nor is there a single price. As part of the Kyoto Protocol, three key “flexibility mechanisms” have been introduced to incentivize participants in emission control. The three mechanisms—JI, CDM, and AAU trading—are described in Figure 25.3.

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It is important to realize the product-market context of carbon markets. While the product in all markets is consistently a tonne of CO2, the terminology and product characteristics differ considerably. The tonne of carbon originating from non-Annex B countries where the CDM mechanism is in operation is called a Certified Emission Reduction (CER), whereas in Annex B countries the picture is slightly complicated. Under the Kyoto Protocol, the maximum amount of GHGs that an Annex B country may emit over the compliance period (2008–2012) is defined as the country’s Assigned Amount (AA). This consists of a number of AAUs, each representing 1 tonne of CO2 equivalent. These AAUs can be bought and sold like any other commodity. Article 3 of the Kyoto Protocol enables Annex B countries to buy and sell AAUs and Removal Units (RMUs), which are gained from government-sponsored sequestration projects, in order to meet their Kyoto targets. Article 6 of the Protocol established the JI flexible mechanism, which allows governments and companies in Annex B countries to buy and sell Emission Reduction Rights (ERRs) from projects that reduce GHG emissions, or sequester carbon from the atmosphere, in other Annex B countries. Thus in Annex B countries the terms AAU, Emission Reduction Unit (ERU), and RMU all mean a tonne of carbon. The tonne of carbon in the EU ETS is called a European Union Allowance (EUA). There are considerable differences within these units, however. While a CER, which is a product in the CDM market, has been eligible for crediting since 2000 and has a lifetime well beyond 2012, its cousin in the Annex B countries is treated differently, and the AAUs do not become eligible for crediting until 2008. The EUAs, which are a product in the EU ETS for Phase I (2005–2007), expire at the end of that phase and do not have value beyond December 31, 2007. A new phase begins on January 1, 2008, when a new CER cycle begins. This is explained later in the section on emissions trading. The simple matrix in Figure 25.4 shows how the two markets JI and CDM relate to each other. Joint Implementation Article 6 of the Protocol established the JI flexible mechanism, which allows governments and companies in Annex B countries to buy and sell ERRs from projects that reduce GHG emissions, or sequester carbon from the atmosphere, in other Annex B countries.


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25.4

Key CDM and JI characteristics

Source: EnerStrat Consulting

As already mentioned, the JI projects become eligible for crediting only from 2008. The development of institutional frameworks is slow. The supervisory body for JI, called the Joint Implementation Supervisory Committee (JISC), was formed only at the first CoP after the Kyoto Protocol in Montreal in December 2005. Since its inception, the goal of the JISC is to catch up with the other two mechanisms, CDM and AAU trading, and provide entities in eligible Annex B countries a transparent and effective regulatory mechanism for the oversight of the JI projects. The main elements of this regulatory process are: ● ● ●

● ●

framing of its own governance processes; establishing a template for a JI project design document; setting out the procedures and mechanisms of public availability and consultations of submitted documents; drawing up the rules of accreditation; designing due processes for quantification from project methodologies, verification of credits, and establishing review process rules and regulations.

Although no JI projects have yet been credited, there is tremendous uncertainty and controversy surrounding them. The key countries, which are expected to host a majority of the projects, are the former Soviet Union (FSU) countries and those Central European countries awaiting accession to the EU. Accession to the EU would imply that these countries would then no longer be within the JI ambit but may be required to join

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the EU ETS. This is leading to uncertainty related to future flows of credits from JI schemes from these countries. Furthermore, since 1990 is the chosen year for determining baseline emissions, there is a considerable body of opinion that Russia and FSU will be in a position of substantial allocation surplus which they can bring to the market, as provider of the marginal allowances, and drive future carbon pricing. This potential emission surplus is what has come to be termed in Kyoto-speak as Russian “hot air.” How this “hot air” will drive pricing is a subject of substantial debate and currently mired in policy uncertainty. The current view among a majority of participants is that by bringing to market the “hot air” AAUs, the industrial sector players in FSU and Russia will force the AAU price down. While this is not an unreasonable assumption, we believe that there are significant barriers to entry for the “hot air” AAUs. ●

●

Very limited effort has been made to actually quantify the volume of AAUs that might come to the market. The process of quantification involves understanding and using an approved methodology for quantification. We are not aware of the level of importance being accorded by industrial players in these countries, given the tight capacity and growing demand. Even if well-documented and quantified proposals were to emerge, there is considerable political opposition emanating from the EU Commission regarding a “simple buy-out” option for the AAUs.

The EU Commission and political leadership in member states have voiced the opinion that unless the transaction involved making energy efficient/green investments in the country of AAU origin, it would not allow EU member-state participants to engage in a “simple buy-out” of AAUs. This process of investing in energy-efficient projects in return for acquisition of AAUs from FSU or Russian participants is called “greening AAUs.” The EnerStrat view is that these twin uncertainties of operations— current inability to identify, quantify, and effectively originate AAUs from creditworthy counterparties in FSU and Russia and policy uncertainty— will impact the forecasted flow of AAUs from FSU and Russian participants. We believe the future National Allocation Plan (NAP) regime in EU member states will force this issue with policy makers. Whether there will be any “lightening of the greening process” still remains an open issue. This is a significant policy uncertainty that needs to be carefully priced


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into AAU transactions, even if the operational uncertainty begins to fade in the short to medium term. Clean Development Mechanism Article 12 of the Kyoto Protocol established the CDM. This enables Annex B governments and companies to buy and sell ERRs from projects that reduce GHG emissions, or sequester carbon from the atmosphere, in non-Annex B countries. For a CDM project to be able to generate CERs, it must first be “registered” by the CDM Executive Board, the bureaucracy set up by the UNFCCC to monitor the CDM. Registration requires a host of criteria to be satisfied, involving due diligence of the project to confirm the voluntary participation of the parties; the project’s contribution to sustainable development; and the project being “additional.” A requirement set by the Kyoto Protocol is that a CDM project must demonstrate that any GHG reductions achieved would not have occurred in the project’s absence, compared to a BAU baseline. EnerStrat believes the CDM Executive Board has interpreted the definition of additionality over-zealously. A central aim of the Kyoto Protocol was to promote financial assistance and technology transfer from the developed world to the developing world through the CDM. Instead, the CDM Executive Board has chosen to limit the number of projects that will take place. There is a risk that this situation will not be rectified in time to restrict the supply of CDM projects, and therefore CER supply, into the Kyoto market before 2012. The importation of credits through the CDM market in the form of CERs is expected to be a major price determinant. A total of 161 projects have been registered which create a flow potential of approximately 52 million CERs/year. An additional 60 projects are awaiting registration, totalling roughly 25 million CERs/year. The first issuance of CERs took place on October 20, 2005. So far, 11 projects totalling 4.5 million CERs have been issued. As against this, the estimated size of the CER market for the years 2008–2012 is around 1.0–1.1 billion CERs, or approximately 200–225 million CERs/year. The fact that CERs are eligible for crediting from year 2000 and have a life well beyond 2012 makes the CER market one of the most attractive and stable (from a regulatory perspective) regimes. The fungibility of CERs with EUAs, once registries are in place and the International Transaction log is fully operational, means that

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CERs have the potential to achieve a price as close as possible to that of the EUAs. However, by definition, CERs originate from non-Annex B markets, and projects in these countries carry other development- and delivery-related risks, and thus CER pricing so far has been in a very wide range. EnerStrat has seen and advised on CER transactions from E5 to E19 per tonne, with varying elements of risk being shared between the buyers and sellers. These CER buyers include governments and carbon funds as well as direct compliance buyers, such as energy utilities and industrial entities. Developers and project participants in CDM projects have to pass their projects through several hoops before they can realize near-EUA prices for their CERs. The process starts with obtaining host country approval from appointed Designated National Authorities on CDM. These authorities are usually national government bodies appointed by the CDM Executive Board, the oversight mechanism for monitoring and establishing CDM processes. Once the host country approval is in place, the project developers have to get their methodology, which quantifies the emission reductions resulting from a particular project, approved and their reductions verified. The CERs then flow on an annual basis into the registries after being issued by the Executive Board. Mechanics of Project-Based Transactions (CDM and JI) If country A has a CDM or JI project generating allowances, country B can use the flexibility mechanism to purchase these allowances. If the project takes place in a non-Annex B country it will generate CERs under the CDM, while in another Annex B country it would generate ERUs under the JI flexible mechanism. CDM and JI projects therefore enable Annex B countries with a Kyoto short to offset their position in order to achieve Kyoto compliance. CDM and JI activities that sequester atmospheric carbon into other carbon sinks can also generate allowances. These projects are known as “land use, land-use change and forestry” (LULUCF). This includes forestry, cropland management, grazing land management, and revegetation projects. LULUCF allowances are only valid once the reductions have been verified by experts and cannot be carried over to a second commitment period (post-2012). An Annex B country can use (foreign-sourced) LULUCF


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allowances from such activities, but it can only use CERs derived from these projects up to a limit of 1% of its 1990 emissions for each year from 2008 to 2012. AAU Trading Under the Kyoto Protocol, the maximum amount of GHGs that an Annex B country may emit over the compliance period (2008–2012) is defined as the country’s AA. This consists of a number of AAUs, each representing 1 tonne of CO2 equivalent. Article 3 of the Kyoto Protocol enables Annex B countries to buy and sell AAUs and RMUs. These are gained from government-sponsored sequestration projects so they can meet their Kyoto targets. If, by 2012, a country has emitted more than its AA, it must make up the difference plus a penalty of 30% in the second compliance period after 2012. Its eligibility to sell AAUs will be suspended until it achieves compliance. We have already discussed AAUs and issues related to greening AAUs in the section on JI. The remainder of this chapter analyzes the world’s pre-eminent carbon emission trading trading scheme, the EU ETS, and the trends seen so far. The EnerStrat view on carbon pricing drivers and the impact of the EU ETS on the European energy and utilities sector is presented. We conclude with some forward-looking views on the future of the carbon markets. THE EU EMISSIONS TRADING SCHEME The EU ETS is Europe’s key market-based mechanism to combat climate change and achieve Kyoto emission reductions at “least cost.” Participation is mandatory and covers approximately 12,000 installations (approximately 5000 plants by ownership) within the 25-member EU, representing 45% of the EU’s total CO2 emissions, or about 30% of the EU’s total GHG emissions. Figure 25.5 describes the key features of the EU ETS. We see that launch of the EU ETS is “on track.” The EU ETS is a cap-and-trade mechanism (described later), and the caps for the compliance period 2008–2012 were being determined at the time of writing. EnerStrat expected that this process would complete some time in the third quarter of 2006. The investment community is seeking guidance on the continuity of the EU ETS beyond 2012 as a mechanism to provide long-term investment

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25.5

EU Commission’s proposal for EU ETS design: on track

Source: European commission proposals paper 2001 and CCC analysis

signals in clean energy assets, carbon abatement technologies, and projects. So far, consultations for the design of the EU ETS beyond 2012 have already begun. EU ETS volumes have touched about 3 million tonnes per day, and prices have grown steadily. As against a volume of 17 million tonnes in 2004, when the EU ETS was not in operation and prices were quoted on the European Carbon Exchange, the EU ETS volumes for 2005 were 362 million tonnes, representing a market value of E7.2 billion. OTC/exchange trades contributed 262 million tonnes, while the remaining 100 million tonnes came from bilateral deals. The EU ETS operates on a cap-and-trade system similar to the US SOx and NOx trading markets, introduced in the Chicago Climate Exchange in the late 1980s (discussed in Chapter 20). On the basis of the legally binding reduction targets which individual member states within the EU have signed, an assigned ministry or a government department (e.g. DEFRA in the case of the UK) is tasked with the responsibility to develop an NAP which allocates reduction targets on eligible participants. This is done on a consultation basis. Once the NAPs are set, they are submitted to the EU Commission. It is these NAPs which


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set the demand for emission certificates. This is a key driver of price for carbon emissions. NAPs for the period 2005–2007 were a test period designed to encourage participants in the EU to familiarize themselves with emissions trading. The NAPs for the second period (2008–2012) were expected sometime in the third quarter of 2006. How the capand-trade system within EU ETS works is described below. Cap-and-Trade System in EU ETS The EU ETS is a “cap-and-trade” system designed to cap installation emissions and allow emission allowances to be traded. Installations covered by the scheme are allocated EUAs; 1 EUA represents the right to emit 1 tonne of CO2. Any person, company, or government can register to trade allowances. The EU ETS is a market designed to be short of CO2 emission allowances in order to force market participants to either reduce their emissions or pay to pollute. This shortfall will be met through trading individual long and short carbon positions within theEU ETS and sourcing additional allowances from within the flexibility mechanisms of the Kyoto Protocol. The EU ETS entered into operation on January 1, 2006, and is already the largest market for emission allowances in the world. It is designed to run in two phases. Phase I (2005–2007) was tailored to initiate the EU member states and companies to the concept of emission reductions and trading emission allowances. Participation was limited to large CO2 emitters in the energy production, steel, cement, glass, tile, and paper industries. Phase II (2008–2012) runs in parallel with the Kyoto Protocol period and may be expanded from Phase I to include other GHGs and carbon-emitting sectors. DRIVERS OF CARBON PRICING IN THE EU ETS The top four drivers of carbon prices in Europe are expected to be the supply–demand balance for emission reduction certificates, abatement technologies driven principally by forward prices of natural gas, supply of emission credits from other markets, and policy developments in the energy-environmental space. For an entity covered by the EU ETS, the actual emissions within the compliance period must be equal to the EUAs it has been allocated.

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The processes for allocation have been based on “grandfathering” of emission rights and are allocated free. The extent of shortness, the difference between allocated EUAs and BAU actual allowances, is a key price driver of the EUAs. The options available for an entity deciding not to curtail its output in order to meet emission allowance volumes are to engage in energy efficiency measures; abatement actions; to purchase extra allowances from the market; or to invest in emission reduction projects in emerging markets which have ratified Kyoto and created mechanisms for transfer of emission reduction certificates generated as a result of the emission reduction projects (CERs). Given that the EU ETS will be short overall, this differential, known as the “carbon balance,” is expected to be a principal driver of price. The EU utility sector, which is the main polluter and covered by the ETS, is expected to be significantly affected by this scheme. In order to minimize its exposure to a high carbon balance, it is expected to shift to less carbon-intensive fuels such as natural gas. The plants most at risk are the fossil fuel intensive coal and fuel-oil burning plants. The option available for utilities is to replace their generation output from coal to more gas. This form of emission abatement essentially changes the merit order of plants. The load duration curve is known as “coal–gas switch.” This coal–gas switching is expected to be the next driver of carbon pricing. Where possible, where spare gas capacity already exists, this form of abatement will impact the carbon price. The third driver is the supply of carbon emission reduction credits from other countries covered in the Kyoto Protocol for either JI or CDM. The CERs from Annex B countries, which are covered by JI, are known as ERUs, and those from non-Annex B countries, covered by CDM, are known as CERs, and each of them represents 1 tonne of CO2. The CERs and ERUs, when fully registered in the emission registries of the EU which track the title transfer of the certificates, are fully fungible with the EUAs. The politics of energy sector restructuring and the policy response to environmental compliance is still evolving within EU member states. The high oil price environment has moved energy politics (security, reliability, and diversity of energy supply) to new levels of activity and intensity. This is likely to drive the growth of key subsectors such as the feed-in laws for renewable energy projects, particularly wind, biomass, and solar, which might develop in southern Europe and would need to be reconciled with the energy market formation in these member states. The manner in which some key southern European countries respond will have an impact on


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25.6

Synopsis of our work on carbon pricing drivers

future generation mix and, consequently, on the likely slope of the emission supply curve. This new uncertainty is a new reality that cannot be wished away. Figure 25.6 summarizes our view on carbon price drivers and our outlook. The rest of this section provides our perspective on primary and secondary drivers of carbon pricing in the EU ETS. Primary Drivers Demand–Supply Balance of Allocations: Market Short The extent of market short is determined by the number of allowances issued in the EU ETS against BAU, which is the amount of emissions that would be emitted based on BAU operation of the underlying eligible assets. The EU countries have issued 6,123 million tonnes of credits through the NAPs for the period 2005–2007. EnerStrat has estimated a small market short of approximately 45–60 Mt/y based on government projections for first period. The BAU projections that underlie these calculations are the same projections used in the NAP process. EnerStrat does not have a view on the future view

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of market short. While the EU Commission announced the publication of its first compliance report on May 15, 2006, we have several questions regarding the breakdown of the numbers expected in the announcement. The companies’ fuel usage may have been considerably different than the historical projections made, primarily due to variances in fuel costs. Gas prices had risen substantially in the past 12–18 months. The NAPs for the second phase were due to be published in mid2006. Until then, it was difficult to quantify the true extent of market shortness, due to reasons mentioned above and the additional fact that there is an uncertainty regarding how governments, who would be making these allocations, would distribute allowances between the trade (EU ETS) and non-trade (aviation, transport, and other sectors not currently covered by EU ETS) sectors. The pricing driver analysis covered in this document pertains to the traded market covered by the EU ETS. Pricing Dynamics Due to Inter-Fuel Competition: Understanding Gas Pricing is Key Currently, the power generation sector, being the recipient of 65% of the total allocation, will share most of the CO2 abatement burden. The underlying basis for the power sector being allowed the higher share of the abatement burden is that there are multiple abatement options available for the power sector, and the ratio of the abatement potential to the number of installations required to be monitored is the most attractive for the power sector. Switching fuel usage for power generation from coal to gas is one of the easiest and least-cost abatement options. Furthermore, EnerStrat believes that the longer-term move toward the gas-fired combined-cycle gas turbine (CCGT) generation power sector is irreversible. Coal and lignite power plants are more carbon-intensive than CCGT power plants. Carbon emissions from a CCGT power plant are just 40% of those from a coal or lignite power plant. Therefore the replacement of coal-fired generation by gas-fired generation is one of the most attractive options available for the power sector after the low-hanging fruit of energy efficiencies have been captured. EnerStrat, however, is of the view that a higher price of carbon (even like that evidenced in July 2005) will not, by itself, provide an investment trigger for new gas-fired capacity. However, for equity investors in new CCGT capacity, a higher carbon price may offer a potential upside.


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The issue that needs careful consideration is the existence and nature of the “feedback” relationship, if any, that may emerge between wholesale power prices and carbon prices. Our rationale that the carbon price may be a “follower” stems from the following. ●

●

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Carbon pricing forms just one, albeit the most exotic and newest, variable that influences gas and power pricing. Other variables range from basic micro-economic drivers of demand–supply, market tightness and concentration, inherent market design mechanisms such as entry/exit pricing, all the way to hard-toquantify variables of security, reliability, and diversity of supply. The European gas and power markets are relatively more mature and liquid than the nascent carbon market. The heavy bias toward the power sector (65% of the allowances) has created considerable information asymmetry among the participating sectors. This has provided an incentive and opportunity for the power sector participants to influence carbon prices.

Figure 25.7 provides some interesting insights into the real relationships between gas, power, and emission pricing; in this case the example F I G U R E

25.7

Gas-power-emission price relationship: Germany

Source: Platts Data and EnerStrat analysis

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is that of German gas power and EU ETS prices. Notice that before the EU ETS became operational, there was no real relationship between carbon and fuel prices. The carbon price throughout 2004 was speculation-driven, triggered by specific regulatory announcements such as those by the IEA, which was conducting simulations and releasing the results. Various EU member states, starting their own carbon procurement funds, also made announcements. The early signs of the inter-fuel relationship actually were evidenced after the launch of the EU ETS. Note the second circle covering the period March 2005 to September 2005. Note also the breakout by the power price (red line) after October 2005. The government was quick to launch an inquiry into the trading activities of the energy majors in Germany. While the correlation appears to be being maintained, the power price movements reflect conductrelated issues in the German power market and may lead to overheating of the carbon price going forward. Secondary Drivers Regulatory Outcome on New Entrant Reserves The NER of the 25-member EU accounts for approximately 300 million credits for Phase I. Thus if new entrants do not arrive into the market, governments will have spare allowances, which they can use to supply the industry or the non-traded sector, which is currently short. If used to decrease their industry shortfall, it will act to force allowance prices down. The shortfall will be met through a combination of the flexibility mechanisms, EU ETS, trading credits from CDM and JI projects, and “hot air” purchases. However, credits from CDM projects, which are the only type of credits which can be used within the first period (2005–2008), are not likely to make any impact on first-phase prices as their volume is expected to be quite low (less than 5 Mt). Therefore supply sources for credits in the first phase are limited. Phase II shortfall will be determined once the second-phase NAPs have been submitted and published by the EU at the end of 2006. However, even though a higher shortfall is expected, the flow generated by CDM and JI projects in the second phase will likely reduce the abatement costs, hence the market may not be extremely short, leading to moderate carbon prices.


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FUTURE UNCERTAINTIES IN EUROPEAN GAS AND POWER AFFECTING CARBON PRICING The abatement curve for every market will differ across the EU member states and will be a function of inherent fuel mix of generation, operating efficiencies, and fuel prices. Market design features, regulatory regimes, and the level of market competition will also feed into the abatement cost curve. There is already a significant variation in market mechanisms, regulatory regimes, levels of vertical integration, interconnection arrangements, and energy market policies within 15 member states of the EU before May 2004. Abatement economics will further change as accession countries come within the ambit of the EU ETS. EnerStrat believes that understanding energy market policies and regulatory and market mechanisms will provide investors a deeper insight into the future energy mix. This understanding will eventually drive the supply of emission allocations and thus the price of carbon. The role of gas, going forward into the future energy mix, emerges as a source of greatest uncertainty. Our recent analyses bear this out. Figure 25.8 clearly shows the gas–carbon emission price relationship, again using the German example. F I G U R E

25.8

Gas–carbon emission price relationship

Source: Platts Data and EnerStrat analysis

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Understanding future gas pricing in an environment where the power sector emerges as the leading user of natural gas is important. Trends in the EU ETS prove a definite link to the coal–gas switch and ultimately to the price of natural gas. Box 25.1 describes EnerStrat’s current view on the medium- to longterm outlook for the European gas market. EMERGENCE OF MULTIPLE CARBON PRODUCT MARKETS IN THE SHORT TERM As described above, a tonne of carbon (whether it be a EUA within the EU ETS, a CER in India or Brazil, an AAU in FSU, or an ERU in JI countries) has a value based on its source of origin; full fungibility for a single global carbon market to evolve is far away, and in this scheme of things the EU ETS has, and will continue to have, a pre-eminent role. As a unified market with its own individual national energy markets, the EU is well suited to develop as a trading market for emissions. The EU ETS market design is the fruition of significant investment of intellectual capital. The European Commission has undertaken a number of simulations. Furthermore, the concept-to-commissioning of the EU ETS has so far been on track, and that it is definitely something to celebrate. Given that there is synchronicity between the evolution of the energy and emission markets in Europe, the EU ETS has the potential and opportunity to become the price-setting market for global carbon, irrespective of the outcome on environmental compliance in the US. WILL THE EU ETS SET THE GLOBAL PRICE FOR CARBON? Two major Kyoto signatories that have committed to significant abatement measures, Canada and Japan, are distant markets and currently exploring their own emission market mechanisms. The fuel mix in both countries is quite different than that of the EU. Japan Japan has been an early adopter, indeed shaper, of the liquefied natural gas (LNG) business in the Asia-Pacific gas basin and has recently faced significant energy market challenges due to an enforced shutdown of nuclear capacity. As a result, gas imports in Japan have risen, and energy efficiency upside potential is relatively limited.


B O X

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25.1

Medium- to long-term outlook for European gas markets Continued price instability • Moderate increase in demand likely to be met by existing and planned resources, thus exerting a potential downward pressure on prices. • Getting gas to market remains a challenge, as a result: • Prices are expected to remain unstable and influenced by uncertainties around nation-states’ energy policies aimed at ensuring security, reliability, and diversity of supply while achieving carbon compliance. • The bargaining power of utilities is expected to grow, leading to possible innovation in design of gas contracts. • We expect short-term trading volatility due to intra-regional discontinuities between gas and power markets. Liberalization drives fragmentation of the value chain and convergence with power • EU directive required liberalization of all parts of the gas market by 2006, leading to possible reduction in transportation margins and more predictable and market price–driven intra-regional gas flows. • However, the scope, process, and status of liberalization differ per country, creating structural and, consequently, pricing discontinuities in the European gas market. • Gas hubs are emerging, enabling growth of trading activities, which will lead to further and sustained correlation between gas and carbon pricing. Source: EnerStat

Furthermore, wholesale market mechanisms for electric power are very new in Japan. Future penetration of CCGT will be an important indicator of abatement economics going forward. Overall, EnerStrat is of the view that Japan will face an uphill challenge for carbon abatement, and therefore abatement costs in Japan may be higher, unless the issues surrounding nuclear generation are not clarified sooner. In the short to medium term, Japan will continue to be an increasing importer of natural gas by both pipeline and LNG. Will Gas Provide the Link for Price Connectivity between Japan and the EU? Over a period of time, as the gas market becomes truly global, the gas price differentials across the EU and Japanese markets will have a significant weight in understanding the carbon price links. For the next 5–10 years, we see the gas market continuing to be regional basin based, with increasing price harmonization driven primarily by LNG trade.

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Canada Unlike Japan, Canada has an active electric power market, given the extensive hydro component of domestic generation and significant power interconnection capacity with the US northeast market. As gas delivery volumes grow in US east/northeast regions, gas prices will become an important factor in abatement economics in Canada. At the moment, it is difficult to see any price connectivity between the Canadian and the EU ETS markets. If Canada decides to set price caps for carbon (a figure of $15/tonne has been bandied about), it would further act as a barrier to price harmonization with the EU ETS. Overall, the EU ETS has been the biggest and the earliest-developed carbon market mechanism. Physical interconnection (or the lack of it), variation in starting point fuel mix, and market design features will act to isolate the price formation in the markets in the short to medium term. Globally, therefore, the EU ETS will be the lead market for price formation. Figure 25.9 explains why we believe that the EU ETS would be the global price-setting pre-eminent market for carbon.

F I G U R E

25.9

Why EU ETS would be a global price-setting market for carbon


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IMPACT ON THE EUROPEAN POWER SECTOR In this section we discuss the impact of imposing binding carbon reduction targets on the European power sector. We have chosen the power sector because it not only is the principal source of energy supply for industry, but is also highly carbon-intensive. The European power sector will be significantly impacted as the reduction targets being imposed on the sector are likely to be severe. This is due to the fact that not only does the power sector have other alternative fuel sources and technologies for generation, but also the relatively small number of assets (compared to all the cars or trucks in the EU, for example) required to monitor can provide the bulk of the required emission reductions. Figure 25.10 describes the starting position of the power sector emissions in 1990, the reference year for all carbon reduction targets. As already mentioned in the earlier sections, the EU participated in the Kyoto negotiations as a single trading bloc and developed its “burden-sharing agreement.” This burden-sharing agreement was a political agreement between member states and was not based on bottom-up F I G U R E

25.10

The starting point: European electricity sector CO2 emissions, 1990

Source: European Union position paper on emissions trading & EnerStrat analysis

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sector-by-sector risk analysis. The EU committed itself to an 8% reduction from 1990 levels by the year 2010. Figure 25.11 provides a breakdown of this 8% reduction target by individual countries, which are now committed to legally binding emission reduction targets. The “growth” economies, such as Spain, were given a “positive” reduction target, while “advanced” EU countries have taken on more onerous reduction targets. While on the face of it, an 8% overall reduction appears within the “achievement” zone, the reality is likely to be very different. The right-hand column of Figure 25.11 provides actual reduction from 1990 levels that the power sector in the individual EU countries would have to make if the country commitments were to be applied pro rata to the utility sector. Given the multiple “technology options” available to the utility sector (e.g. renewable and non-conventional sources of energy generation, such as wind or solar), the likelihood is that the utility sector will be forced to take more than a fair share of the burden.

F I G U R E

25.11

The burden-sharing agreement: from political agreement to commercial reality

Source: European Union position paper on emissions trading; DRI; EnerStrat analysis


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This makes a seemingly modest 8% reduction actually quite an onerous responsibility at 28%! The 15 member states of the pre-enlargement EU are committed to reducing their combined emissions of GHGs by an average of 8%, compared to 1990 levels, over the Kyoto Protocol period. This combined commitment was distributed to individual member states through the European burden-sharing agreement. The 10 new member states have adopted targets under the Kyoto Protocol (except for Malta and Cyprus) and will be full participants in the EU ETS. Figure 25.12 articulates one possible scenario emerging from the above simulation of applying pro rata Kyoto targets to the European electricity sector. Almost 100 GW of capacity could potentially be at risk of possible shutdown or significant reduction in generation output. This is not a trivial issue for the utilities. Expect to see some unprecedented asset portfolio restructuring in the European electricity sector, especially in the fossil fuel–intensive regions of Germany, Spain, and Italy.

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25.12

Fossil-fired power generation capacity at risk

Source: EnerStrat analysis

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CONCLUDING REMARKS: POST-2012 SCENARIO While the overall message of this chapter is that the carbon market is a commercial reality and no longer entrenched in the political realm, the policy influence continues to be a dominant feature of this commodity and is expected to remain so. Any reasonable assessment of the post-2012 scenario for the carbon market is heavily dependent upon how the policy level changes impact the only functioning carbon trading scheme of any substantial size and depth: the EU ETS. This chapter has discussed the various scenarios for a carbon market beyond 2012. This would mean attempting to articulate a range of scenarios from one extreme (that the carbon market becomes truly globalized by 2012) to another (which could be a meltdown of the existing EU ETS). This is obviously difficult given the nascent market and the uncertainties at the policy level, even just within the EU ETS. Understanding how the policy and market developments play out within the EU, key Annex B countries such as Canada and Japan, and large fossil-intensive, high-growth, non-Annex B economies, particularly Brazil, Russia, India, and China (the BRIC economies) in the Phase II period is therefore key to forming a viewpoint on the carbon markets after 2012. We have articulated three scenarios to create a synthetic view that carbon markets will certainly continue post-2012. However, carbon markets will not become truly global by 2012, nor is the likelihood of a meltdown very high. The end game is somewhere in between. It is too soon to tell what exact shape it might take. Figure 25.13 raises some beliefs that underlie the likely possible scenarios for the global carbon markets post-2012. Our view is very much that of the EU ETS being a significant driver of carbon markets around the world. Lessons learned in the EU ETS should stand players in good stead going forward. The emergence of a value on the price of carbon is beginning to provide the impetus for next-generation energy policy. A value on the price of carbon has an effect of rethinking on all aspects of the energy value chain, and this rethinking is happening within the energy supply side as well as on the consumer side. On the supply side, there are new carbon reduction choices, including Enhanced Oil Recovery (EOR) married to carbon sequestration as well as the re-emergence of the nuclear power option. The carbon market


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25.13

Post-2012 carbon markets: possible scenarios

gives a fillip to natural gas and LNG becoming the fuel of choice for future energy. We still view natural gas as a rich man’s fuel. Even King Coal, long viewed by many in the energy sector as a sunset industry, is coming back with a vengeance in a new avatar: clean coal! Energy markets are being deregulated and subjected to open competition. Still, they continue to be increasingly policy-driven, which does not make them any more predictable. Investors are looking for long-term investment signals, and the carbon market, thanks to its infancy, clearly cannot provide them on its own. While this chapter was being written, the EU ETS suffered its first major plummet, a price crash from a high of E31/tonne to a brief hit at E9.28/tonne, and “restabilized,” albeit temporarily, at E16/tonne. We expect more volatility. The EU ETS is here to stay and still remains the most cost-effective abatement option. It remains to be seen whether investors lobbying for long-term investment signals from carbon markets would succeed in establishing a “carbon tax” as a more effective means of guaranteeing regime stability in carbon markets.


C H A P T E R

26

Entrance of Energy and Environmental Hedge Funds Peter C. Fusaro and Gary M. Vasey

INTRODUCTION Why are hedge funds now attracted to the energy industry? One reason is that overall hedge fund returns have disappointed investors over the past several years, and investors are seeking new areas of investment where returns may be better. The volatile world of energy is seen by the funds as potentially providing such an opportunity. In early 2006, there were more than 8,100 hedge funds with at least $1 trillion invested in markets—double the number of 1999. That number is rising as pension funds and other institutional investors look for greater returns and diversification of their financial risk. The energy industry fits the investment profile of hedge funds and is under intense scrutiny and investment interest. It is simply a matter of risk/reward as higher returns are available in the financially immature energy sector. Ironically, hedge funds trading oil today are not doing anything very different than the large investment banks such as Goldman Sachs, Bank of America, Barclays, or Morgan Stanley already do. The proprietary trading desks of these and other large investment banks are actually “hedge funds in drag,” just as Enron was. The banks never talk about what they do, and consequently, they tend to fall under the radar screen. But they must be doing something right as the Morgan Stanley and Goldman Sachs 393

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commodity trading groups have had substantial profits in recent years and continue to do so. After the shock of the demise of Enron and merchant energy trading generally, it has taken more than three years to rebuild energy trading. The warnings that the industry would be in turmoil for many years were likely overly exaggerated, but also, predictions regarding the demise of the market were wrong. We have not seen the predicted globalization of electric utilities; instead, we have seen foreign utilities retreat from US power markets. We also have witnessed the Wall Street power companies rise as they continue to buy distressed assets and trade those assets through various asset-optimization strategies. We have seen the resurfacing of the financial institution/utility joint ventures with Merrill Lynch’s purchase of Entergy/Koch, and more recently, we have seen the entrance of financial hedge funds focusing on the energy industry, particularly energy trading which had the largest market vacuum. What has occurred is the merger of big money and big energy. This is no longer a game for small companies, including electric and gas utilities. This is investment banking, asset-backed trading, pure commodity trading, and most importantly, that trading of financial balance sheets that assumes “capital at risk.” Our research has revealed that there are more than 460 hedge funds focused on the energy sector. These include pure energy commodity trading on established exchanges, such as the New York Mercantile Exchange (NYMEX) or ICE Futures, and overthe-counter (OTC) markets, commodity/energy equity plays, distressed-asset plays, debt plays, and various other financial undertakings. That number continues to grow as international markets are beginning to attract hedge fund interest into the energy sector beyond the USA and the UK as Zurich, Frankfurt, Scandinavia, Singapore, Tokyo, India, Australia, Hong Kong, and Shanghai are established as not only energy trading centers, but hubs of hedge fund activity. The number of new hedge funds entering the energy as well as the environmental financial complex continues to grow each day. ENERGY HEDGE FUNDS The world of the hedge fund is both somewhat secretive and largely unregulated, except in the USA, making it difficult to get a clear and true measure of just how significant its impact really is on energy markets. Many hedge funds trade through banks, making it difficult to disaggregate data sufficiently.

CHAPTER 26 Entrance of Energy and Environmental Hedge Funds

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Therefore the evidence for the activities of hedge funds has to be gathered somewhat indirectly; some of the best evidence is to be found in market activity, such as increased open interest on the NYMEX for open crude oil, heating oil, gasoline, and natural gas futures contracts, primarily due to hedge fund trading activity and a smaller amount of volume on the ICE Futures (formerly called the International Petroleum Exchange). Many of our findings by necessity rely upon anecdotal evidence from various energy traders, bankers, brokers, energy analysts, and hedge funds. Investment management professionals have been using managed futures for more than 30 years. In fact, there are more than 450 commodity trading advisers (CTAs) actively trading in energy, managing over $70 billion in assets that look similar to hedge funds; a proportion of hedge funds are registered as commodity pool operators. However, our research also has identified new energy-specific hedge funds being created to trade both physical and financial energy commodities and, as importantly, a growing number of macro hedge funds that have shifted significant proportions of their assets into energy in the past two years. Hedge funds, CTAs, and pension funds are now estimated to control over $200 billion in the energy market. While small in market share, its significance is magnified because they are very active traders as opposed to energy companies that are naturally long or short in energy markets and use futures to hedge themselves against adverse price movements. These players also use financial leverage, which amplifies their trading influence. Inspection of data from the US Commodity Futures Trading Commission (CFTC)—which regulates futures exchanges, including NYMEX—demonstrates that non-commercial investors in 2005 gambled on higher prices and accounted for over one-half of all oil futures bets, a rise of 300% over 2002. We have seen similar increases in natural gas futures trading by the funds as well. Both daily trading volumes and contract open interest on the NYMEX set daily volume records for energy futures, while International Petroleum Exchange data show around a 20% increase in open interest for Brent crude oil futures since 2004. However, CTFC data reveal futures and options positions only on the NYMEX and do not reflect OTC energy markets at all. The best indicator of OTC trading activity is the increased trading activity on the NYMEX’s Clearport, where a record volume of trading continues. Buying on a commodity index is another strategy used by the hedge funds. This strategy allows the investor to profit on rising energy prices and other commodities without the risk of either financial futures or physical

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commodity trading. It is estimated that more than $60 billion is tied up in index trading today, largely in the four major indices: Goldman Sachs Commodity Index, Dow Jones–AIG Index, CRB Reuters Index, and Deutsche Bank Index. Energy commodity markets are being driven by news more than ever before, thus creating significant volatility. While there has been much media commentary on the possibility that energy prices currently display a speculation premium, our analysis of the fundamental geopolitical risk and supply–demand factors for each of the major energy commodities suggests that these have dictated recent upward price movements. In our view, hedge funds and other speculators have simply followed that trend. However, the higher sustainable prices for energy will have an impact across and beyond the energy world. Ironically, the combination of higher energy prices and the current state of the energy industry in North America is creating a further opportunity for other hedge funds in energy. Hedge fund activity in energy includes equities, distressed-asset plays, debt, and emissions and renewable energy credit trading. They are increasingly entering into equity investments as well. While it is difficult to measure succinctly the impact of the hedge funds’ activities in energy, our research concludes that there is significant evidence to suggest that the funds and the investment banks are here to stay, bringing back both liquidity and a risk-taking culture to energy markets. In fact, we have concluded that there are many more funds forming throughout the world to take advantage of continuous price volatility driven by supply tightness and higher-than-expected demand. Traditional energy utility companies are either exiting or becoming further marginalized by these activities. On the other hand, many of the funds do not understand energy, and although they have sophisticated tools and models, there remains a very real danger that this lack of specific energy knowledge and modeling will result in further market fallout at some stage in the near future. Thus many funds experienced blowups in natural gas trading in September and October 2005 due to the impact of Hurricane Katrina. Several lost more than $100 million apiece trading natural gas futures and OTC contracts. WHY ENTER ENERGY NOW? This is not the first time that hedge funds have tried to enter the energy trading markets. In the late 1990s, there was a concerted effort by the NYMEX to entice hedge funds to become more active in energy futures trading. That effort failed when only two hedge funds responded. It should


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be remembered that this was early in the cycle of hedge fund activity and that energy always has been perceived by the financial world as a “stepchild” in that it is a much smaller financial market with its own commodity-related exposures and a very complex physical market. It was just not attractive to hedge funds at that time. They remained active in energystock equity trading and still are. During 2001 and 2002, hedge funds were very active shorting both Enron and the merchant power-sector stock equities. They made massive amounts of money on the financial debacle that ensued. During 2003, they had money to put to work in energy but did not see the equity opportunity. The electricity blackout of August 14, 2003, in the US northeast and Canada brought more fund interest but no major investment as they still were interested in equity investment plays; moreover, there are few investment opportunities in power reliability and alternative energy plays. Continued oil market price volatility in 2004 and 2005 was the catalyst for the hedge funds. Frankly, the daily news reports in all media of energy market price volatility drove the attention of funds to trade commodity energy. This has been the opening to the transformation of financial energy markets as the funds provide liquidity and exacerbate price volatility. Their presence has escalated intra-price volatility and increased trading volumes and open interest in oil futures contracts. They also became more active in North American gas futures trading in 2004. It is this movement to trading commodities that was new. The current financial energy markets are the culmination of 28 years of energy trading, but we still are trading only on a notional basis $2.2 trillion of paper energy compared to the over $4 trillion of physical business1 (notional is the outstanding value of all energy contracts on both energy futures exchanges and the OTC markets). We have a long growth trajectory ahead. Today, we are seeing a sustained bull market for oil, gas, and coal globally due to rising global demand for fossil fuels. This will continue for several years due to supply constraints and robust energy demand. We are seeing a resurgence of interest in coal trading and the globalization of that market, and the impact on electricity fuel supply cannot be understated as that market continues to grow as well. We observe both physical electricity trading and many distressed-asset plays in both the USA and Europe in the electric power sector by the funds. We are seeing even more esoteric plays in green trading with carbon and renewable energy trading hedge funds beginning to trade those markets as they emerge. Energy commodity markets have become characterized by increasing prices and price volatilities. Moreover, the general business environment

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in a post-Enron world is spurring previously unseen interest in energy equities and assets. While oil markets continue to boom as a result of geopolitical issues, supply–demand factors, and weather risk, the other commodities have followed suit. North American natural gas supply and production declines have resulted in higher sustainable prices and increased and sustained price volatilities. Meanwhile, robust demand for coal also is apparent as generators eye the higher costs of generating electric power using natural gas as a base-load fuel (this is impacting the emissions trading market as well). Electric power is seeing renewed trading interest as well due to its price volatility and inability to store. Hedge funds view energy markets as providing the opportunity they seek to obtain greater returns. Likewise, investment banks have a risktrading culture, deep pockets, and access to both physical and financial traders. Even energy merchant companies with surviving trading arms are now seeking to partner with investment banks to sustain and improve trading operations while obtaining access to increased expertise, more sophisticated tools, and risk capital. Moreover, the multinational oil and gas companies have the balance sheet to put their capital at risk. It is no accident that BP is the number one gas trader and in the top five in power trading in the USA, with an almost $2 billion trading profit in 2005. They have the balance sheet and supply access to play in this new financial market. We believe that the next few years will see the accelerated financialization of global energy trading markets. The investment banks and hedge funds will eventually sell back their generation assets to utilities as the supply surpluses are burned off. The emergence of a global climate change regime will directly bring new financial risks to the utility patch as the Kyoto Treaty is implemented in 2008. Risk is now more pervasive in the energy patch than ever before. The multi-commodity market that has been talked of for many years, with its multiplicity of risks, has finally arrived. We will see more hedging of fuels such as oil, petroleum products, natural gas, ethanol, biofuels, and coal. Environmental risk hedging (e.g. carbon and greenhouse gases) will take its place alongside the sulfur oxide and nitrogen oxide markets of today. We will hedge financial renewable energy, and there are now several green hedge funds involved in trading carbon dioxide and renewable energy credits. We will hedge negawatts (the value of energy efficiency) as demand response regimes come into maturity and show a financial benefit of energy efficiency linked to carbon reductions. But most importantly, we will see markets that work and a more sophisticated and savvy financial


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form of energy risk management emanating from New York, where it all started. And in Europe, London as a financial trading center will benefit from this uptick in energy trading. Speculative energy trading has a strong future, but it will likely not be the traditional utilities and energy merchants that will create and maturate that market. While much of the energy industry has returned to the relative safety of trading around assets and marketing activities, energy markets have become characterized across all energy commodities by increasing prices and price volatilities. OIL TRADING MARKET OPPORTUNITIES The more established oil futures and OTC markets are the most attractive for hedge fund trading as they are more liquid and have ease of access and exit. There are a variety of factors that currently are influencing high oil prices and greater oil-price volatility. These factors include unusually high geopolitical risk among members of the Organization of Petroleum Exporting Countries (OPEC), continued economic growth in China and India tied to oil, rising US gasoline demand, and the lack of needed investment in oil exploration and production by the oil majors. Each of these factors is interconnected and is leading to the current high-oil-price environment. Oil demand has surprised many analysts by the extent of its growth, particularly in 2004, but that sustained demand is still in evidence. The International Energy Agency projected 2005 oil demand growth of 2.5 million barrels per day (b/d) or 3.2 percent over 2004’s 81.1 million b/d (International Energy Agency 2004). It seems that the world economy finally has recovered from the shocks of September 11, 2001. Oil consumption in China and the USA is driving much of this increased demand, with demands at 7 and 21 million b/d, respectively. STRUCTURAL CHANGES IN COMMODITY TRADING We believe that global energy commodity markets are undergoing a fundamental structural change. For each energy and energy-related commodity, global and regional markets are displaying a new level of supply-demand tightness. As a result, we have argued that energy commodity prices will remain high for some time to come and would ratchet higher

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due to global refining capacity constraints. In London, we found this to be a view readily shared by the bankers and hedge fund contacts, but apparently not by oil company traders. We heard comments that recent relatively small downward movements in oil prices were symptomatic of mean reversion. Indeed, many oil firms are still holding on to internal oil price forecasts of around $25 to $30 per barrel and, at times, seem to be in denial of the current supply and demand environment. There may be mean reversion, but in our opinion it will be reversion to a higher mean! At a time when OPEC’s ability to be the “swing” producer is diminished and when Asian nations such as China and India are busy securing supply to feed their increased domestic energy consumption, one is forced to ask why the major oil companies appear to be looking for a low-price future? Only Saudi Arabia among OPEC members has any real spare capacity, estimated at 1.5 million b/d, and there are increasing questions over the accuracy of that estimate among industry pundits since its reserve estimates have never been audited by an independent third party. At the same time, major oil companies continue to downgrade their own reserves estimates and have singularly failed to add any major new finds over the last decade or so (as evidenced by Shell and El Paso downgrades during the past year). Estimates from Deustche Bank also suggest that oil companies have reduced their exploration budgets by more than a quarter, while the International Energy Agency has calculated that about $2.2 trillion needs to be spent on exploration and production between now and 2030 if future oil demands are to be met. There is now a dichotomy in views on where future oil and other commodity prices are headed. On the one hand, the oil companies and their traders see a return to a lower price, while the “speculators” view a price of $40 to $60 (or more) per barrel as being the norm. A recent poll on our Web site, the Energy Hedge Fund Center (www.energyhedgefunds.com), illustrates the same dichotomy of views, with two peaks of $20 to $30 per barrel and $40 to $50 per barrel. In the fourth quarter of 2004, hedge funds and other speculators went long on oil prices, and their gamble paid off in the form of good returns. They were right, and the oil companies were wrong. WAITING FOR MEAN PRICE REVERSION Perhaps the oil companies have been caught wrong too many times in the past, when price rises proved to be temporary spikes, and are focused too much on the price collapses in 1986 and 1998. In those instances of price spikes in 1973–1974, 1978–1979, and 1990–1991, incremental monies


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spent on exploration and production resulted in diminished returns as the oil price reverted to levels that made new developments sub-economic. On the other hand, could it be that exploration is too much risk for oil companies to bear? After all, increased exploration activities and therefore risk have simply not been rewarded by analysts and Wall Street. Instead, Wall Street likes share buyback programs and large dividends. In fact, in recent years, majors such as BP and ExxonMobil apparently have spent more on share buybacks and other programs designed to improve share price than they have on their exploration and production activities. The irony is that the oil industry has prided itself for decades on its risk-taking acumen. It seems today it is most influenced by accounting and economic models that usually look at the past and not the future. On the surface, the evidence seems to suggest that major oils, having been conditioned into a behavior set based on meeting or exceeding the expectations of Wall Street analysts in a prolonged regime of low oil prices, find it difficult to see and/or react to the sweeping and fundamental shift in markets we are witnessing now. Given the current supplydemand tightness (as opposed to supply shortages) and projections for increased demand in Asia and other markets such as the USA, any potential for supply disruption through industrial dispute, terrorism, or natural disaster is likely to have a serious impact on prices. The speculators see this clearly and are betting significant sums on it. In the meantime, if the majors delay too long in revising their views of future oil prices, the situation will simply become worse. Recently, they have announced steppedup investment programs in exploration, production, and refining, but there is a time lag when the benefits of this new investment will impact the oil and gas markets. In the interim, we expect continued periods of supply tightness leading to price spikes and then price retrenchments, as we have seen since 2004. Perhaps the easiest way for major oil companies to increase reserves in the short term is to acquire them in the ground via the acquisition of independents. However, even on this front, the oil firms seem to be lagging the speculators as investment banks already have been actively buying reserves in the ground. Morgan Stanley is reported to have purchased 24 million barrels of reserves for $775 million from Anadarko Petroleum over the next four years and, in conjunction with Deutsche Bank, to have purchased equity North Sea production as well between 2007 and 2010, among others. Plainly, they see an opportunity for profit in their activities. In fact, we see the banks getting more active in physical oil, gas, and power markets.

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When we look to the future of oil supply, we assume that it is oil companies that have the expertise and know-how to find, develop, and exploit new reserves. In fact, we are relying on them to do so. Today, however, both the anecdotal evidence that we have collected and the published evidence suggest that the investment banks and hedge funds are ahead of the majors in terms of understanding that we have entered, almost unannounced, a new paradigm of oil supply and demand. From where we sit, the speculators have it right, and if the oil companies do not react and respond soon, then supply issues could take on even more importance in the coming years. WHAT IS A HEDGE FUND? “Hedge fund” is a general, non-legal term that was originally used to describe a type of private and unregistered investment pool that employed sophisticated hedging and arbitrage techniques to trade in the corporate equity markets. Hedge funds traditionally have been limited to sophisticated, wealthy investors (also called “high new worth individuals”). Over time, the activities of hedge funds broadened into other financial instruments and activities. The term “hedge fund” now refers not so much to hedging techniques, which hedge funds may or may not employ, as to their status as private and unregistered investment pools. Hedge funds are similar to mutual funds in that they both are pooled investment vehicles that accept investors’ money and generally invest it on a collective basis. Hedge funds differ significantly from mutual funds. Historically, most hedge fund managers have not been required to register with the US Securities Exchange Commission (SEC) and therefore have not been subject to regular SEC oversight. However, in December 2004, the SEC issued a final rule and rule amendments that require certain hedge fund managers to register with the SEC as investment advisers under the Investment Advisers Act by February 1, 2006. Between 3,000 and 4,000 hedge funds registered during February 2006. Furthermore, hedge funds are not subject to the numerous regulations that apply to mutual funds for the protection of investors, such as those requiring a certain degree of liquidity, the ability to redeem mutual fund shares at any time, the protection against conflicts of interest, assurance of fairness in the pricing of fund shares, disclosure regulations, and limitations in the use of leverage. This freedom from regulation permits hedge funds to engage in leverage and other sophisticated investment


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techniques to a much greater extent than mutual funds. Although hedge funds are not subject to registration and all of the regulations that apply to mutual funds, hedge funds are subject to the anti-fraud provisions of the federal securities laws. There are also US anti-money laundering provisions that all funds must disclose to the federal government. In legal circles, it is anticipated that more hedge fund regulation will be coming as they begin to penetrate retail markets for investments—some funds are known to be soliciting as low as $5,000 for investment. This makes them look more like mutual funds. It is also anticipated that hedge fund regulation will move beyond the USA into other countries as well. In the USA, hedge funds generally rely on Sections 3(c)(1) and 3(c)(7) of the Investment Company Act of 1940 to avoid registration and regulation as investment companies. To avoid having to register the securities they offer with the SEC, hedge funds often rely on Section 4(2) and Rule 506 of Regulation D of the Securities Act of 1933. The recent SEC financial disclosure requirements are really light-handed regulation used to assuage public concerns over financial markets and have had some impact on hedge fund investment. What is really required is greater transparency of investors and investments but not disclosure of fund trading strategies or any other proprietary information.

TYPES OF HEDGE FUNDS According to Eichengreen and Mathieson (1999), there are really three major classes of funds: macro funds that take large unidirectional positions based on a top-down analysis of macroeconomic and financial conditions; global funds that take positions worldwide but employ bottom-up analysis; and relative-value funds that take bets on the relative prices of closely related securities. Funds also are classified according to the strategy that they adopt: relative-value, event-driven, or “other” strategies. The five types of relative-value strategies include the following. 1. The equity-market-neutral strategy seeks to profit by exploiting price inefficiencies between related securities, neutralizing exposure to market risk by combining long and short positions. 2. Convertible arbitrage involves purchasing a portfolio of convertible securities and hedging a portion of the equity risks by selling short the underlying common stocks.

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3. Fixed-income arbitrage is a market-neutral hedging strategy that seeks to profit by exploiting pricing inefficiencies between related fixed-income securities while neutralizing exposure to market rate risk. 4. The fixed-income strategy involves investment in noninvestment-grade debt. Objectives may range from high current income to acquisition of undervalued instruments. Emphasis is placed on assessing credit risks of the issuer. Some of the available high-yield instruments include extendible/reset securities, increasing rate notes, pay-in-kind securities, step-up coupon securities, split-coupon securities, and usable bonds. 5. The mortgage-backed fixed-income strategy invests in mortgagebacked securities. Many funds focus on AAA-rated bonds. Event-driven strategies include the following. 1. Distressed-securities strategies invest in, and may sell short, a company’s securities where the price has been impacted by a distressed situation (e.g. reorganization, bankruptcy, distressed sales, or other corporate restructuring). 2. Merger arbitrage is sometimes called risk arbitrage and involves investment in event-driven situations such as leveraged buyouts, mergers, and hostile takeovers. Among the other strategies are the following. 1. Equity hedge is comprised of long stock positions with short sales of stock or stock index options/futures; it has a long market bias. 2. Sector composite involves investment in specific sectors, primarily long energy, financial, health-care/biotechnology, real estate, and technology sectors. 3. The emerging-markets strategy invests in the securities of companies or the debt of developing or emerging countries; its investments are primarily long. 4. Global macro strategies involve leveraging investments on anticipated price movements of stock markets, interest rates, foreign exchange, and physical commodities. 5. Short selling involves the sale of a security not owned by the seller and is a technique used to take advantage of an anticipated price decline.


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WHY THE HEDGE FUND FACTOR IS HERE TO STAY Markets need speculation. Markets will adapt to more speculative trading. The funds look for liquid markets, such as energy, and price uncertainty. Energy, particularly oil trading, fits that bill. They are betting on long-term risk, and they have found it in the energy patch. Hedge funds, on the other hand, are finding that energy is only one place to put their money to work. But in reality, it is difficult to know how much hedge funds have contributed to previous market declines or how much they are fueling the rise in oil prices today. What their impact really appears to be is an increase in intra-day price volatility and trading volumes. Some energy players expect hedge funds to try to push contracts outward. One impact seems certain. The energy markets have become more volatile since 2004, as speculators and large institutional investors, frustrated by the lackluster equity and bond markets, turn to oil in search of richer returns. Their activity is helping to move crude prices faster and farther than market fundamentals would seem to warrant—and not always in the expected direction. Both the unpredictability of price movements and the unexpectedly rapid 50% jump in oil prices during 2004 can be traced in part to major growth in trading activity in the oil markets. However, speculators do not set the price, although they do intensify price movements in either direction beyond what the fundamental factors normally would warrant. But to look at oil inventory levels, as many analysts still do, begs the question, which is that oil markets and, to a lesser degree, North American gas market prices are becoming more volatile due to the increased trading by funds and investment banks. From our current research, it is impossible to quantify how much of that capital went into energy trading, but it has been substantially risk capital. It is still accelerating. HOW ARE OIL AND GAS PRICES DETERMINED? Crude oil prices are currently a combination of market fundamentals and market psychology. Fundamental drivers within the crude market are a combination of the domestic and international. In general, domestic West Texas Intermediate (WTI) crude prices are determined by: 1. US stocks of crude oil and petroleum variance from a five-year average;

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2. 3. 4. 5. 6. 7. 8. 9. 10.

OPEC production variance from quotas; strategic petroleum reserve variance from targets; US gross domestic product growth; demand factors; OPEC spare capacity (e.g. Saudi Arabia); US refinery capacity variance from the maximum; the US federal funds interest rate; the US dollar; hedge funds.

In addition to using these market fundamentals, hedge funds currently appear to be a good proxy for expected WTI crude prices due to a strong correlation between hedge fund positions and price movements in the US WTI crude markets. Because hedge funds have entered into the commodities market recently, they appear to be increasing daily price volatility. In other words, they are amplifying daily price movements on both energy futures exchanges. However, arguments have been made recently using trading data that the hedge fund activity may in fact be reducing market volatility. While the jury is still out on hedge funds and volatility, we believe that both add speed and price volatility to energy markets. Although the correlation between crude and natural gas prices may not remain constant in the long term, indicators suggest that gas prices will continue to be strongly correlated with WTI crude prices. There are many bullish factors supporting elevated WTI prices, such as the constant threat of import supply disruptions and the general perception of world supply shortage. Moreover, hedge fund interest in commodities with net long positions is likely to increase. Among other factors are the extreme upside potential of equity market shifts to the commodities market, simultaneous work economy growth (especially China), and inventory levels that are expected to remain below historical norms, although they have been building as of early 2005. Further, gasoline and sweet crude will likely remain tight (with a wide sweet–sour crude price spread), and growth in world oil supply will be in the medium sour category. The average sulfur content will be 1.1–1.2%, which will create problems for the US as sulfur requirements are 0.5% for gasoline production under the Clean Air Act. Additionally, the Russian export program is marked by uncertainty. Finally, there is the continued lack of US refinery capacity. No new refineries have been built in the US since 1976.


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Given the number of bullish factors supporting a high WTI price and the current strong correlation with natural gas prices, it would not be surprising to see continued very bullish natural gas prices. It will be important for all concerned with natural gas prices not only to understand and monitor gas fundamentals and psychology but also to be well versed in crude oil markets. Gas markets now are heavily influenced by many interrelated and global factors in the commodity and financial markets, including coal, electric power, liquefied natural gas, crude oil, emissions, and hedge fund markets as well as regulatory and public policy influences. Yet understanding natural gas markets means understanding not only fundamental drivers but also cross-commodity relationships and prevailing market psychology. These additional factors can lead to seemingly counter-intuitive results. Factors that normally would determine the direction of natural gas prices no longer seem to be having a significant impact. Rather, almost every natural gas price movement, either up or down, has been linked to corresponding moves in the price of crude oil (WTI). Natural gas spot prices as well as NYMEX future prices have been very difficult to rationalize based solely upon what would be considered “natural gas fundamentals.” From a fundamental analysis perspective, this relationship between natural gas and crude oil during summer months is less intuitive than during the winter period, when there is competition between the two commodities for heating purposes. However, while there is no strong fundamental reason for crude prices to move natural gas prices, crude oil and natural gas prices historically have been correlated at irregular intervals, and correlation has become today’s market psychology. There are a number of factors intuitively that would support higher price levels for natural gas, including forecasts of declining production and a projected increase in demand. ENERGY TRADING IS NOW REBUILT For many years, floor traders on the NYMEX have complained about hedge funds entering the energy markets. For the most part, they were wrong. Today, the funds have finally arrived. They are looking for greater returns on equity for their investors than the flat trading of stock market equities. The missing ingredient is the understanding of energy markets and its complexity. Funds like to “move money and move money out (of markets),” as one experienced energy trader commented for our research.

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However, what they are overlooking is that there are now fewer trading opportunities for that type of trading and that there are greater risks in the market because they have arrived to trade. Another seasoned energy trader commented that “there is a billion dollar fund with three traders; the oldest is 29 years old.” The funds lack knowledge and experience in energy markets. That is going to change as the market further matures. Energy trading is the most volatile and complex of any commodity. As noted earlier, energy prices are driven by supply–demand fundamentals, technical trading, weather, events, geopolitical issues, and regulatory issues. Credit risk is still an important risk to manage since the energy industry has lower credit, and more so since the downgrades in the utility sector. The funds have better credit and less knowledge, although the credit issue is now rising among their counterparties as there are concerns about with whom the companies are trading. Moreover, they also appear to have a know-it-all attitude. These factors bode for more impending energy-trading disasters. While funds have lower costs of capital and lower overhead, the fear is that funds are financially unstable due to their very short-term perspective. Hedge funds primarily are organized as private partnerships to provide maximum flexibility in constructing a portfolio. They can take both long and short positions, make concentrated investments, use leverage, use derivatives, and invest in many markets. This is in sharp contrast to mutual funds, which are highly regulated and do not have the same breadth of investment instruments at their disposal. In addition, most hedge fund managers commit a portion of their wealth to the funds in order to align their interest with that of other investors. Thus the objectives of managers and investors are the same, and the nature of the relationship is one of true partnership. This is quite a bit different from energy traders on the trading desks of banks or energy companies. Traditionally, hedge funds traded the stock equities; however, in 2004 they entered energy commodity trading in a big way and have remained in the sector. The hedge funds are gambling that the energy complex will continue to exhibit price volatility as the stock markets have basically traded sideways for the past several years. While many of the investment banks are now moving into the physical oil, gas, and electric power businesses, hedge funds generally stay out of the physical market, although this is now starting to change. This drive for greater profits in energy trading is also altering the nature of risk-taking. Banks are feeling more confident that they can place longer-term bets on which way oil and gas prices will go. For example,


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in the past, power traders have felt comfortable going out only about 18 months in the future. However, the longer the contract, the greater the chance to earn revenues on a well-chosen trade when armed with sophisticated risk management and modeling systems that have been honed in the derivatives markets. There are many positives in bringing in the larger financial players because new participants bring more market liquidity to the table. Banks and funds not only have increased liquidity, in the banks’ case they also have greatly improved the risk profile of the entire sector. Instead of trades being conducted by companies teetering on junk-level ratings, many deal counterparties are now highly rated financial institutions with large balance sheets. As more banks and funds enter energy trading and try to push the envelope, it is unsurprising that many long-term players view the newcomers with suspicion, arguing that their presence is causing increased volatility. Because funds and banks have made highly leveraged bets that prices will stay at high levels, the argument is that something of an energy bubble has been created. So far, their strategy has appeared to pay off in the present bull market. However, we have seen this all before. It is still early in this round of hedge fund activity in energy as we see the following trading plays: (1) crude oil futures on exchanges and OTC markets in the USA and Europe; (2) natural gas futures and OTC markets in the USA; (3) heating oil (gas/oil) futures trading; (4) gasoline futures trading; (5) electricity trading; (6) coal trading; (7) distressed generation asset plays; (8) midstream oil and gas acquisitions; (9) emissions trading, particularly carbon dioxide; (10) renewable energy trading; (11) water trading (both equities and commodities); and (12) weather derivatives. Of all of these, crude oil dominates trading as it is the most liquid market. The funds have increased intra-day trading and price volatility accordingly. They have no physical positions to cover, so they are pure speculators. This has upset some oil traders and refiners. Moreover, there are many small hedge funds that are being seeded by larger funds. There are essentially two main types of funds entering energy trading: the macro funds, with assets under management often in excess of $2 billion, that now have a proportion of their funds in energy; and the energy-specific funds created to trade energy by ex-merchant energy traders. It is the former—the macro fund traders with black boxes and macro models—who are essentially clueless about the underlying complexity of energy. They follow market trends using black-box algorithms,

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and while so far, many have done well in crude oil futures markets, at least one took a bath by shorting the market. Disturbingly, our research has revealed that there may be a good deal of ignorance and perhaps even some arrogance on the part of these wellcapitalized yet relative neophytes new to energy trading. Energy trading and risk management form the most complex, volatile market in the world. Its prices are influenced by weather, geopolitical factors, supply–demand fundamentals, news, and many other elements that cannot be quantified into simple black-box algorithms. Many of these funds are quite small and should have modest effects on energy-trading markets. There is no threat of systemic risk in energy trading from the funds. Meanwhile, the energy-specific funds, often a good deal smaller in terms of assets under management, frequently are founded and led by ex-energy traders. Our research has identified numerous such funds, mostly set up in the recent past, and with new energy-specific funds being announced with increasing frequency, they represent an identifiable trend. In general, these funds are not limiting themselves to energy commodities markets but are using their energy-industry knowledge to participate in physical markets and other energy commodities, including electric power and natural gas. In fact, one such fund apparently made its investors around 20% in its first month of operation. Plainly, the entrance of hedge funds is reigniting the energy trading phenomenon. By increasing liquidity through the introduction of additional risk capital and by improving the counterparty credit situation with strong balance sheets, the funds are providing the market some positives. However, the lack of detailed physical energy knowledge and reliance on black-box models by some in the hedge fund community, combined with the lack of transparency of their activities, ought to cause some unease and concern as well. The last thing the energy markets need is yet another speculative trading-led implosion. WHAT THE FUTURE HOLDS Oil markets promise to continue to be both volatile and higher priced for the foreseeable future, with violent selloffs from time to time based on news and event risks. The key price driver is rapidly expanding demand driven by economic recovery in the world economy. The wild card is the extremely tenuous security situation in the Middle East that could cut off Iraqi, Iranian, Nigerian, or Venezuelan oil supplies. This situation is likely to continue indefinitely. We are still not yet seeing


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much of an energy conservation effect due to higher prices as Chinese demand, coupled with US gasoline demand, continues to soar. The prognosis is for higher and more volatile prices. Rising oil demand, geopolitical risk, hedge funds, low oil inventories, and price uncertainty will continue to fuel the markets. A supply disruption caused by terrorists cannot be ruled out and could raise prices to $100 per barrel in the short term, and $100 calls on oil were written during March 2005. A significant terrorist act could undermine both economic growth and oil demand similar to post-September 11 impacts on global markets. Global oil demand, while still robust, is starting to show signs of slowing as economic growth slows primarily due to higher oil and other commodity prices. While the supply–demand fundamentals still underpin high oil prices, speculative investment, primarily through hedge funds, is exaggerating prices to the upside. However, slower economic growth, particularly in the USA, could undermine higher prices as a “conservation effect” takes place. We have not seen that since 1979–1980 during the Iranian oil crisis. Natural gas trading markets are emerging as global markets and are influenced by coal, power, liquefied natural gas, emissions, crude oil, fuel oil, and regulatory factors as well as by hedge funds. Although hedge funds are only one element of many, they can influence daily price movements in North America. Many of the funds trade not only in energy commodity futures contracts but also in OTC oil contracts or commodity index funds that are offered by the funds or large investment banks. When the bank or trader places financial risks of this kind on an exchange, US regulators classify them as “commercial” volumes as opposed to the “non-commercial” category of big money funds. It is important to put the scale of the energy markets in perspective to the funds. It has been estimated that the value of all outstanding NYMEX contracts is equal to 0.1% of the US equities market. There is tremendous opportunity for growth in this area, not only in commodity markets but also in energy equity markets. Some hedge funds are trying to arbitrage this play between the commodity and energy equity prices. Following the development of independent system operators, electric power trading is essentially a physical power market with a small electricity derivatives market. The fact is that very few traders can handle 3,000 – 4,000% price volatility. This has made financial power trading both a short-term market and one requiring a physical presence.

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Having said that, some hedge funds are entering financial power trading in 2005 and 2006. They will probably fail. In 2005 and continuing into 2006, the second round of energy hedge funds entering and expanding their participation in the energy complex began. Besides trading oil, gas, power, and coal as commodities, they now trade sulfur dioxide, nitrogen oxide, and carbon dioxide emissions as well as renewable energy credits. They are buying distressed generation assets, oil and gas reserves in the ground, and energy debt. They are trading weather derivatives. And most recently, there are several hedge funds ready to trade long-dated water rights in the western US. The greatest bull market in natural resources now under way is sustainable due to relatively low energy prices in real dollars, globalization of markets, and higher and sustained price volatility. Already, several energy and natural resource funds of funds have entered the markets (these are hedge funds that invest in other hedge funds). They are looking for returns and also looking at subsectors in the markets, such as tanker rates and spread trading for new opportunities. Usually, they wait for several years of market maturation and performance records before they invest. However, in the energy sector, the old rule book has been thrown out. We will see a piling on of energy hedge funds entering this sector with the attendant great returns. But the fact is that energy trading has changed forever as the markets now move into more mature financial markets. Hedge funds add to that financial sophistication. ACKNOWLEDGMENTS The authors would like to thank the International Research Center for Energy and Economic Development for the use of material from their occasional paper Hedge Fund Change Energy Trading (2005) by Peter C. Fusaro and Gary M. Vasey. REFERENCES International Energy Agency (2004) Oil Market Report. Available at http://www.oilmarketreport.org. Eichengreen, B and Mathieson, D (1999) Hedge Funds: Do We Really Know? Washington, DC: International Monetary Fund.

NOTE 1. Energy Hedge Fund Center, LLC estimates (www.enegyhedgefunds.com).

C H A P T E R

27

Forward-Looking Energy and Environmental Trading Market Developments Peter C. Fusaro

INTRODUCTION Energy risk management began with the New York Mercantile Exchange (NYMEX) launch of the No. 2 heating oil contract in 1978. Environmental risk management began in 1995 with the launch of sulfur dioxide (SO2) emissions trading in the USA. The continued market development of both energy and environmental financial trading has created an entire worldwide industry for risk management. Oil is already a global market, and now natural gas, through increasing global use of its liquefied form, will create a global gas trading market over time. There are now traders, brokers, software vendors, data providers, industry traders, institutional investors, financial traders, hedge funds, and investment banks participating in its growth. In 2006, the Energy Hedge Fund Center LLC estimated the notional value of all energy derivatives at $2.2 trillion and rising. Environmental financial markets were over $20 billion in carbon and SO2 trading and are rapidly globalizing. The estimated market capitalization for carbon trading may be $3 trillion in 20 years. It is already more than doubling each year. The use of alternative energy is rising rapidly from a small installed base but at sustained growth rates of 30–40% for wind and solar power. It now seems inevitable that due to the multiplicity of risks in global energy, environmental markets will give rise to more trading, more risk management, and more risks. The irony is that oil markets have so many 413

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market drivers that analysts cannot predict the future based on the meanreverting price models of the past. The bottom line is that we now have rapidly globalizing markets for oil, natural gas, coal, carbon, and renewable energy. Electric power will probably still remain a local or regionalized market due to the inability to store this real-time commodity. It, too, is increasingly financializing. The speed of trading is also accelerating. Hedge funds trade price volatility. They are speculators. They add liquidity to markets but also exacerbate price volatility. Traditional oil and gas traders have been slow to react to this change. There will be more fund-driven intra-day price volatility in more markets as hedge funds move beyond New York, Houston, and London. They will increasingly assist in the commoditization of not only energy markets but also environmental financial markets. They have money, which is the “fuel” of trading. We are beginning to see other significant structural changes in the energy complex that we have never seen before. One is the increased movement of investment banks from the financial energy business into the physical energy business. Morgan Stanley now runs the West Coast jet fuel operations of United Airlines because of its credit facility. Morgan Stanley is also the largest SO2 emissions trader in the USA and does not own a power plant. Goldman Sachs has moved aggressively into ownership of electric power stations, into renewable energy (Horizon Energy, with 4,000 MW of wind power), and now also into cellulosic ethanol (with a minority stake in a plant in Canada). Other banks are moving into the physical energy and environmental business. It makes good business sense. Hedge funds are also becoming suppliers of private equity in many power plant deals. Some funds are buying not only carbon credits but also the power plant that makes the credits. The energy and environmental financial markets are rapidly changing. We think that the banks are on to something. Frankly, there is a paucity of superior energy and environmental risk management talent. This is barely an academic discipline on the global scale needed. What will probably occur is outsourcing of the risk management function to banks. This change will not be called “outsourcing,” but the banks will provide the fixed price deals, collars, and other structures. Most companies cannot compete for the talent, do not have the credit facility, have poor knowledge of the energy or environmental risk management space, and will stick to their core competency. Some very sophisticated energy companies, such as BP, will offer similar risk management structures

CHAPTER 27 Forward-Looking Trading Market Developments

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and probably tie that more and more to physical supply contacts through structured finance. But hedge funds will not provide this service. They exist to make returns for investors. Looking out over the horizon for the next 15 years, we see an energy industry working very hard to find fossil fuels at higher prices to meet growing year-by-year demand. We see an alternative energy industry expanding rapidly to hundreds of thousands of megawatts of capacity with trading built in through renewable energy derivatives. But the fact is that the world is flattening, with globalization not understood by many. The Internet is beginning to change everything, including energy and environmental trading. The closure of the oil trading floor in London in April 2005 was accepted with ICE Futures going totally electronic. It is now part of history. The NYMEX now stands at the precipice of offering both floor and electronic trading through its new relationship with the Chicago Mercantile Exchange, the world’s largest futures exchange. Some of this is in response to the fund-driven trading which constitutes conservatively over half of energy trading and thrives in an electronic environment of anonymity. Much has been made over the years of the need to look at energy risk management as a fiduciary responsibility of energy companies. We can now extend that role to environmental risk management too. Corporations are now assessing their environmental financial risk vis-à-vis climate change or emissions footprints. These risks are increasingly being managed by the trading mechanism and structured finance. The future is not like the past. This century is symbolic of a time of rapid change, where the models and templates of the past no longer work. Oil prices around the $20 mark are history because the whole world is consuming fossil fuels faster than ever before. The whole world is also consuming renewable resources at ever increasing rates. Economic development is coupled to energy consumption once again. Big companies are slow to move and have trouble playing catch-up. The first mover days of Enron are gone. There are no first movers anymore. Fear of change does that. But things do change. Markets do move faster. Prices do gyrate more. Carbon is becoming the new gold when people can actually figure out what they are trading and how to trade it. It requires a new skill set. We foresee commodity trading continuing to grow in oil, globalize in natural gas and coal, accelerate in carbon, and grow rapidly in renewables. All of these markets and their cross-commodity components will need risk management techniques. They will need the trading infrastructure coupled with risk controls for proper execution.

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Energy is indeed a risky business. The risks can be summarized as the following: ● ● ● ● ● ● ● ● ● ● ●

market risk; price risk; event risk; weather risk; credit risk; liquidity; performance; tax; technology; operational; environmental.

Organizations such as the Professional Risk Managers’ International Association have a tremendous opportunity to spread the gospel of energy and environmental risk management through training, teaching, and knowledge transfer. The new age of risk management is upon us. It is not linear. And it is accelerating. We see the bull market for energy continuing for some time to come. We do not know how just long it will last, but the industry fundamentals simply have not changed, and when one factors in the environmental issues and opportunities, we see the tremendous investment opportunities lasting longer than many might expect. To us, this means that if you think you are late into energy as an investment opportunity, you are probably wrong. However, that does not mean that some of the early returns can be repeated, just that energy and environment can still provide superior returns as compared to other more traditional opportunities. It also means looking and working a little harder for those returns. What has been the hardest obstacle for many energy analysts and investors to comprehend is that it is different this time. Most energy companies and governments have continued to look backward and wait for the expected mean price reversion to lower energy prices. They saw that happen with the price crashes of 1986 and 1998 and expect it again. It has not occurred and will not this time. There is no surplus supply cushion this time around in these demand-driven markets. The profit picture continues to stay bright for energy companies, and the perception


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may change into one that this is the greatest energy bull market of all time. Global energy demand continues to rise, and global refining surpluses are almost exhausted, leading to a summer of higher highs due to capacity tightness. Savvy investors know a good thing when they see one. Energy is that new asset class. ENERGY HEDGE FUNDS There are many conflicting views on where energy commodity prices might be headed. Some are still looking for mean reversion, while others foresee extortionate highs in the near future as the production of oil peaks and can no longer keep up with demand. We look for sustained higher and more volatile energy commodity prices for several years as a result of global and regional supply–demand tightness. This means that simple trend-following strategies might not produce the returns that have been seen in recent years as prices moved in one direction—up. Certainly, there will be directional opportunities in different commodities and markets, but not like the one we have just experienced. Instead, we expect to see an increase in various spread and arbitrage strategies to make money in energy commodities. The volatility we have experienced in the commodity markets will remain for some time to come. To us, this also means that hedge funds will have to work harder to profit and that their strategies will need to pay off in both up and down markets. Today, the funds have arrived. Funds like to “move money in and move money out,” as one experienced energy trader commented. However, what they are missing is that there are now fewer trading opportunities for that type of trading and that there are greater risks in the market because they have arrived to trade. The funds often lack knowledge and experience in energy markets. Energy trading is the most volatile and complex of any commodity because of the physical underlying market with many risk factors. These factors bode for more impending energy trading disasters, and we saw some in North American gas markets in August and September 2005 as funds tried to short the market and lost hundreds of millions of dollars as prices spiked due to Hurricanes Katrina and Rita. While funds have lower costs of capital and lower overheads, the fear is that some funds are financially unstable due to their very short-term perspective. Hedge funds are primarily organized as private partnerships to provide maximum flexibility in constructing a portfolio. They can take both long and short positions, make concentrated investments, use

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leverage, use derivatives, and invest in many markets. This is in sharp contrast to mutual funds, which are highly regulated and do not have the same breadth of investment instruments at their disposal. In addition, most hedge fund managers commit a portion of their wealth to the funds in order to align their interest with that of other investors. Thus the objectives of managers and investors are the same, and the nature of the relationship is one of true partnership. This is quite a bit different from energy traders on the trading desks of banks or energy companies. NATURE OF RISK-TAKING IS CHANGING This drive for profits in energy trading is also altering the nature of risktaking. Banks are feeling more confident that they can place longer-term bets on which way oil and gas prices will go. For example, in the past, power traders have felt comfortable going out only about 18 months in the future. The longer the contract, the greater the chance to earn revenues on a well-chosen trade. Armed with sophisticated risk management and modeling systems that have been honed in the derivatives markets, banks are pushing out the futures market further into the future. The funds are following this trend. Even with the hedge fund blow-ups in 2005, with crude oil options trading, natural gas squeezes, and electric power financial trading failures, we are not seeing much concern about credit issues, except for some large bank credit analysts. The issue of credit risk and hedge fund performance is rising in importance, but management of hedge fund credit risk is an issue no one wants to talk about in public, including the ratings agencies. Today, Moody’s KMV, Fitch, and Morningstar are just venturing into rating hedge funds with their announcements to provide these services. Moody’s KMV has been working on their own scoring model for hedge funds as they think the business might suffer from their inability to get position information from hedge funds. They have only rated one energy hedge fund, as far as we know. This issue would require both monitoring and a more proactive approach. Once again, the ratings agencies have been slow to react. As more banks and funds enter energy trading and try to push the envelope, it is unsurprising that many long-term players view the newcomers with suspicion, arguing that their presence is causing increased volatility. Because funds and banks have made highly leveraged bets that prices will stay at high levels, the argument is that something of an energy


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bubble has been created. So far, their strategy has appeared to pay off in the present bull market. However, we have seen this all before. Of course, there are other immature energy commodity markets where there can still be sustained upward price movements—for example, the downstream side of the business where chemicals and plastic feed stock markets are more immature and as energy prices in general feed through. But the largest opportunities are likely to be in the emerging emissions and renewable energy markets, as previously discussed. Water is the next logical market extension for hedge fund trading in both equities and as a commodity. It can be priced. It can be stored. It can be traded. Water is another capacity-constrained commodity that hedge funds are delving into, looking for both arbitrage and opportunity. It is a space worth watching as rising environmental concerns push the envelope here. Three billion people in the world do not have access to potable water. We have already seen several water equity hedge funds and one commodity trading water fund trading longer-dated water rights in the US West. One billion dollars of water rights are ready to trade in Australia. There are water hedge funds in Israel. Water is the next financial market. The next play for investors will be greener investments in the energy space as the environmental financial market matures rapidly over the next several years. Once again, investment opportunities will be alternative energy equities, environmental companies, green hedge funds, and green MLPs (Master Limited Partnerships). Emissions trading and renewable energy trading are truly emerging markets, but they are also starting to mature. There is also a need for more investible indexes that are both passive and active as a means for investors to play the green card. We think that the clean technology play should not be underestimated as venues for further investment in alternative energy equities, renewable energy projects, credit and emissions trading, and water accelerate. OTHER MARKET CHANGES Given today’s energy issues, there are innumerable opportunities for investors in energy and energy-related areas. Hedge fund interest in uranium, coal, steel, sugar and emissions demonstrates this. It is not just interest in playing the commodity, but also in investing in the assets and equities of the companies involved. At the same time, there is a lot of interest in the creation of new commodity indices, new ETFs (Exchange Traded Funds), and structured

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products that will allow investors and hedge funds in particular both to find new places to put money to work and to offer hedging and diversification plays. We know of multiple initiatives in this area that will bear fruit in the coming months and years. THE FUTURE IN ENERGY AND ENVIRONMENTAL TRADING WILL BE DIFFERENT The future for energy and environmental trading has never been brighter. Energy market fundamentals have now changed, requiring better information and knowledge to anticipate these changes due the accelerated speed of energy markets. Information moves markets. Technology has not only flattened the world, it has eliminated barriers to entry. We trade on information flow. Event risk is everywhere in a global market economy. The seamlessness is the Internet. The glue is money. We are now entering the third year of expansion by both banks and energy hedge funds into more areas of energy and environmental trading. The mantra has always been superior risk-adjusted returns in new investment areas. This book has identified many of these areas of trading opportunities. CONCLUSIONS Energy is a risky business and has become even riskier with the advent of more hedge fund trading. As the markets change in many ways, both physical and financial, they require new strategies to manage risk. This time, it is different, and human nature does not accept change. Today’s energy markets do not follow traditional seasonality. They are following too many factors to quantify in multi-factor models. Black-box, trend-following trading ignores “commonsense.” Applying these mechanistic trading methods of hedge fund managers used to trading the more mature and well-established foreign exchange markets does not necessarily work in energy or environmental trading. While the terms in the energy complex— such as futures, options, and swaps—are the same, the reality is that the energy financial markets are also very physical and complex. They react to underlying supply-demand fundamentals and are complemented by weather risk as well as other risk factors. But how long this bull market play will last is anyone’s guess since we have never been here before.


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The end-of-oil crowd is just plain wrong. We are in not in the twilight of oil since the cost base has risen and money will drive further production from higher-priced sources globally. But higher prices will at some point also bring a “desired” conservation effect. That has not happened to any great degree yet, except anecdotally in the press. But in 2008 we will have more than ample supply. We are not running out of anything. The price is just higher. Consumers need to accept it and manage their risk better. Environment is an even more immature financial market that is just now getting traction globally. Green hedge funds are few, but many are considering entry into this new field of opportunity in alternative energy, renewable energy trading, and emissions trading. As this takes hold globally, these emerging financial markets will have even more risk to manage as lock-up periods increase and streams of environmental credits are monetized. The monetization of credits through such green finance schemes will jump-start more market liquidity as hedge funds become the new providers of both equity and liquidity in this next emerging market. Oil, gas, power, and coal exhibit tremendous daily and annualized price volatility, and this seems to be increasing. The liquidity provided by the hedge funds is evidenced at the front end of the markets through both NYMEX and International Petroleum Exchange oil and gas futures trading but is much more established in the OTC energy markets. Like the hedge funds themselves, these markets are not regulated and have a degree of price opaqueness. However, the current market has new entrants that are not well known. It is not highly levered. It has less investment from high net worth individuals and Family Office and will gain more liquidity as the shift to institutional investment gains traction as well. There will need to be increased allocations to hedge funds in the emerging energy and environmental financial markets. Energy and environmental hedge funds are that place, as smaller funds can take advantage of arbitrage opportunities in socalled niche strategies, while larger funds can provide equity for projects, physical assets, debt, and distressed assets as well as trading. We are already seeing the convergence of hedge funds and private investing in the clean technology space, for example. Diversification into the energy and environmental sector will prove to be a shrewd strategy for hedge fund investors. Traditional asset classes are unlikely to generate enough alpha to meet return expectations. The recurring returns will get their alpha from less efficient markets, such as energy and environment.

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Energy trading is a business with tremendous risks and rewards, but one thing has always been certain: size matters. Energy trading and risk management is the most complex, volatile market in the world. Its prices are influenced by weather, geopolitical factors, supply–demand fundamentals, news, and many other factors that cannot be quantified into simple black-box algorithms. Plainly, the entrance of hedge funds and more investment banks is reigniting the energy trading phenomenon. By increasing liquidity through the introduction of additional risk capital and by improving the counterparty credit situation with strong balance sheets, the funds and banks are providing the market many positives. Those that get it right most of the time are the multinational oil companies and the big two investment banks, Morgan Stanley and Goldman Sachs. These entities have maintained a consistent presence in energy trading markets for decades and have the knowledge base to put many of the hedge funds to shame. The energy trading winners will be those two banks and some savvy energy trading companies that know both the energy markets and risk. This is a true Darwinian game. This is survival of the energy fittest. Except for a handful of funds, they just cannot stack up against the great oil trading companies, such as Vitol or BP, and the investment banks, and since energy trading is a zero-sum game, the wealth transference could be massive. Count on more great quarters for the commodity shops of Morgan Stanley and Goldman Sachs. The other investment banks, such as Merrill Lynch, Barclays, Bank of America, and Deutsche Bank, are now playing catch-up. Consistency and people make profits, not poorly executed trading strategies and opportunistic occurrences. The positive value of the hedge funds is that they are bringing liquidity and a risk-taking culture back to the energy complex. Traditional energy companies, such as the utilities, are either exiting or being marginalized in this emerging, more sophisticated financial trading environment. The funds will also hire all those unemployed energy traders and have recentered New York and London as the twin capitals of the energy trading world. The golden age of energy and environmental trading and risk management is upon us. Watch it blossom into a thousand flowers.

G L O S S A R Y

A Additionality. This occurs when there is a positive difference between the emissions that occur in the baseline scenario and the emissions in a proposed project, in the context of the Joint Implementation (JI) and Clean Development Mechanisms (CDMs) outlined in the Kyoto Protocol. AFRA. Average Freight Rate Assessments. A monthly estimate of tanker rates issued by London tanker brokers, AFRA, quoted on a world-scale basis; assists large oil companies’ internal accounting, provides a freight element for some netback deals, and serves other purposes somewhat removed from the daily tanker business. Aggregation. The policy under which all futures positions owned or controlled by one trader or a group of traders are combined to determine reportable positions and speculative limits. American-style option. An American-style option may be exercised at any time during its lifetime, up to and including the expiration date. See European-style option. Annual cap. In a gas buyer’s purchase agreement, there is often a limit higher than the annual contract quantity (ACQ) above which the seller is not liable to sell. This is the annual cap and is usually stated as a percentage of the ACQ. Also known as maximum annual quantity (MAQ). API gravity. An arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the American Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter the compound. Light crude oils generally exceed 38 degrees API, and heavy crude oils are commonly labeled as all crude oils with an API gravity of 22 degrees or below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. ARA. Amsterdam–Rotterdam–Antwerp area. A port and refining area in the BelgianDutch region. A cargo or barge of a refined product traded on a CIF ARA basis means that ports within this area are covered in the cost. A cargo traded on a FOB basis means the oil can come from any of these ports. Arbitrage. The simultaneous purchase and sale of similar commodities in different markets to take advantage of price discrepancy. Arbitration. The procedure of settling disputes between members or between members and customers. Asian option. Asian (or average) options have payoffs that depend on an average of prices for the underlying commodity over a period of time, rather than the price of the commodity on a single date. The averaging period may correspond to the entire life of the option or may be shorter. 423

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Glossary

Asphalt. A mixture of heavy carbon-based compounds containing a high percentage of multiple-ring aromatics, many of them involving sulfur, nitrogen, and oxygen atoms. Some folks use the word “asphalt” interchangeably with bitumen, the name of its characteristic constituent. Note: The conversion factor for asphalt is 5.5 barrels per short ton. Associated-dissolved natural gas. Natural gas that occurs in crude oil reservoirs either as free gas (associated) or as a gas in solution with crude oil (dissolved gas). See Natural gas. ASTM. American Society for Testing and Materials. An organization which determines and publishes consensus standards of suitability and quality for a wide variety of materials, including petroleum and refined products. ASTM develops and endorses methods of testing hydrocarbons properties as well as definitive specifications for such classes of refined product as fuel oils, aviation kerosene, burning kerosene, and motor gasoline. Atmospheric distillation. A technique for separating hydrocarbon mixtures which uses a distillation apparatus operated at atmospheric pressure. Generally, the industry specifies ambient pressure to distinguish products of crude distillers, atmospheric fractions, from the products of vacuum flashers which, as the name implies, distill atmospheric residue in a partial vacuum. At-the-money option. (a) At-the-money spot: An option whose strike is the same as the prevailing market price of the underlying rate or price. (b) At-the-money forward: An option whose strike is at the same level as the prevailing market price of the underlying forward contract. Average option. See Asian option.

B Backhaul. A tanker’s revenue-producing return voyage. Some ships shuttle between two tanker ports. They travel in one direction as dictated by normal oil flow patterns or refining systems’ needs. Often, they have no natural employment from when they discharge to their port of origin where another load awaits. They would like to find a cargo to pay their costs on this return trip. Otherwise, they must return in ballast. Charters often relet ships at bargain backhaul rates for these voyages. They prefer some income to none. Back month. Back month contracts are any exchange-traded derivatives contracts apart from the nearest, or front, contract month. Back-testing. A strategy is tested or optimized on historical data, and then the strategy is applied to new data to see if the results are consistent. Backwardation. When the price of nearer (typically prompt or spot) crude, product, or another underlying commodity or instrument trades at a premium to the same commodity or instrument traded further forward. Also known as an inverse. See Contango. Bar chart. A chart that graphs the high, low, and settlement prices for a specific trading session over a given period of time.

Glossary

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Barge lots. Quantities of petroleum product accommodated in the sizes of barges in common use in a particular area. This term usually applies to small (less than cargo-size) volumes of product intended for regional distribution. On the US Gulf Coast, for instance, petroleum product barges typically range from 10,000 to 50,000 barrels. On the Rhine, barges typically carry lots as large as 1,000 tons. Barrel. A unit of volume equal to 42 US gallons. Barrel of oil equivalent (BOE). Gas volume that is expressed in terms of its energy equivalent in barrels of oil. Six thousand cubic feet of gas equals one barrel of oil equivalent (BOE), or 42 US gallons of oil at 40º Fahrenheit. Barrels per calendar day. The amount of input that a distillation facility can process under usual operating conditions. The amount is expressed in terms of capacity during a 24-hour period and reduces the maximum processing capability of all units at the facility under continuous operation to account for the following limitations that may delay, interrupt, or slow down production. See Barrels per stream day. Barrier option. Barrier options are exotic options which either come to life (are knocked-in) or are extinguished (knocked-out) under conditions stipulated in the option contract. The conditions are usually defined in terms of a price level (barrier, knockout, or knock-in price) that may be reached at any time during the lifetime of the option. Baselines. Estimates of population, GDP, energy use, and resulting greenhouse gas (GHG) emissions, without policies and measures that address climate changes as well as the impacts of climate change without remedial policy. Baseload. The minimum amount of electric power delivered or required over a given period of time at a steady rate. Base stock. A hydrocarbon mixture which makes up much of the volume of a gasoline blend. Usually, such stocks have properties not too far removed from finished fuel because the minor components have to bring the entire blend within accepted limits of gasoline quality. Base stocks in today’s US motor gasoline include cat gasoline, reformate, and alkylate. Base year. Targets for reducing GHG emissions are defined in relation to a base year. In the Kyoto Protocol, 1990 is the base year for most countries for the major GHGs. Basis. The difference between spot (cash) prices and the futures contract price. Unless otherwise specified, the price of the nearby futures contract month is generally used to calculate the basis. Basis risk. The risk that the value of a futures contract (or an over-the-counter hedge) will not move in line with that of the underlying exposure. Alternatively, it is the risk that the cash-futures spread will widen or narrow between the times at which a hedge position is implemented and liquidated. Basis swap. Basis swaps are used to hedge exposure to basis risk, such as locational risk or time exposure risk. For example, a natural gas basis swap could be used to hedge a locational price risk: the seller receives from the buyer a NYMEX Division settlement value (usually the average of the last three days’ closing prices) plus a negotiated fixed basis and pays the buyer the published index value of gas sold at a specified location. Basket trades. Large transactions made up of a number of different stocks.

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Glossary

Bear market (bear/bearish). A market in which prices are declining. A market participant who believes prices will move lower is called a “bear.” A news item is considered bearish if it is expected to result in lower prices. Bear spread. In most commodities and financial instruments, the term refers to selling the nearby contract month and buying the deferred contract to profit from a change in the price relationship. Benchmark crude. Synonymous with reference crude or marker crude. A crude oil whose price is used as a reference against which other crudes are priced. Because of their liquidity, the NYMEX light sweet crude oil and IPE Brent crude oil futures contracts are used as global benchmarks. Dubai crude is widely used as a benchmark for Middle Eastern crudes, especially for sale to Asian markets. Beta. A regression of the estimated coefficient that belongs to a particular variable. Beta (coefficient). A measure of the market/nondiversifiable risk associated with any given security in the market. A ratio of an individual’s stock historical returns to the historical returns of the stock market. If a stock increased in value by 12% while the market increased by 10%, the stock’s beta would be 1.2. Bid. An expression indicating a desire to buy a commodity at a given price. See Offer. Bid and ask. Highest price and lowest price that an investor will pay for a tradable. Bilateral energy trading. Trading whereby two parties (for example, a generator and a supplier) enter into a contract to deliver electricity at an agreed time in the future. Bilateral netting. An agreement between two counterparties to offset the value of all in-the-money contracts with all out-of-the-money contracts, resulting in a single net exposure amount owed by one counterparty to the other. Bill of lading. Documentation associated with a specific cargo of oil and signed by the captain of the ship and the contract supplier. Biodiesel. A renewable fuel synthesized from soybeans, other oil crops, or animal tallow that can substitute for petroleum diesel fuel. Biofuels. Liquid fuels and blending components produced from biomass (plant) feedstocks, used primarily for transportation. Biogas. A medium BTU gas containing methane and carbon dioxide, produced from the anaerobic decomposition of organic material in a landfill. Also called biomass gas. Biomass. Nonfossil material of biological origin constituting a renewable energy resource. Included in wood and waste. Biosphere. The portion of the Earth and its atmosphere that can support life. The part of the global carbon cycle that includes living organisms and biogenic organic matter. Bitumen. Mineral pitch rich in asphaltenes and other complex, high-molecular-weight molecules. These mixtures of heavy hydrocarbons and resins form the base of, and impart adhesive, semi-solid consistency to, asphalt cement and tar. Black box. A proprietary, computerized trading system whose rules are not disclosed or readily accessible. Black-Scholes option pricing model. An option-pricing model initially derived by Fischer Black and Myron Scholes in 1973 for securities options and later refined by Black in 1976 for options on futures.

Glossary

427

Blender. Someone or some organization that combines various components to produce motor gasoline. Blendstock. A component combined with other materials to produce a finished petroleum product. The term applies most frequently to motor gasoline ingredients. Block trades. Large transactions of a particular stock sold as a unit. Bottoms. Unvaporized material drawn from the lowest point of a fractionation column. Breakaway gap. When a tradable exits a trading range by trading at price levels that leaves a price area where no trading occurs on a bar chart. Typically, these gaps appear at the completion of important chart formations. Breakout. The point when the market price moves out of the trend channel. Brent Blend crude oil. UK Brent Blend is a blend of crude oil from various fields in the East Shetland Basin. The crude is landed at the Sullom Voe terminal and is used as a benchmark for the pricing of much of the world’s crude oil production. Broker. A company or individual that executes futures and options orders on behalf of financial and commercial institutions and/or the general public. Broker-dealer. A firm that handles transactions for its customers and also purchases securities for its own account, selling them to customers. Brokerage fee. A fee charged by a broker for executing a transaction. Brokerage house. An individual or organization that solicits or accepts orders to buy or sell futures contracts or options on futures and accepts money or other assets from customers to support such orders. Also referred to as “commission house” or “wire house.” Broker’s deck. Orders physically held by the floor broker in the trading pit. BTU (British thermal unit). A standard unit for measuring the quantity of heat energy equal to the quantity of heat needed to raise the temperature of 1 pound of water by 1º Fahrenheit at or near 39.2° Fahrenheit. The BTU is a convenient measure by which to compare the energy content of various fuels. See Heat content of a quantity of fuel, net. BTX. An abbreviation for benzene, toluene, and xylene. Bull. Someone who thinks market prices will rise. Bull market (bull/bullish). A market in which prices are rising. A market participant who believes prices will move higher is called a “bull.” A news item is considered bullish if it is expected to result in higher prices. Bull spread. In most commodities and financial instruments, the term refers to buying the nearby month and selling the deferred month to profit from the change in the price relationship. Bunker C. A residual fuel used as ship’s fuel; usually has a high sulfur content and high viscosity. Butane. A normally gaseous straight-chain or branched-chain hydrocarbon (C4H10). It is extracted from natural gas or refinery gas streams. It includes isobutane and normal butane and conforms to ASTM Specification D 1835 and Gas Processors Association Specifications for commercial butane. Butterfly spread. The placing of two interdelivery spreads in opposite directions with the center delivery month common to both spreads. Buy and hold. The acquisition of a tradable for the long term rather than quick turnover.

428

Glossary

BuySell. A swap in which, for accounting purposes or other reasons, company A sells a parcel to company B, while B sells a second parcel to A. Each party buys one and sells another. Buying hedge. Buying futures contracts to protect against a possible price increase of cash commodities that will be purchased in the future. At the time the cash commodities are bought, the open futures position is closed by selling an equal number and type of futures contracts as those that were initially purchased.

C California Environmental Quality Act (CEQA). A California law that sets forth a process for public agencies to make informed decisions on discretionary project approvals. The process helps decision makers to determine whether any environmental impacts are associated with a proposed project. It requires that environmental impacts associated with a proposed project be eliminated or reduced and that air quality mitigation measures be implemented. Cap and trade. A policy that allows large amounts of emissions from a group of sources to be controlled at a lower cost than if the sources were regulated individually. The approach first sets an overall cap (or maximum amount of emissions per compliance period) that will achieve the desired environmental effects. Then authorizations to emit in the form of emission allowances are allocated to affected sources, with the total number of allowances within the cap. C+F. Cost and freight. The price includes the cost of the cargo and the freight/vessel hiring costs but not the insurance. Calendar spread. The purchase of one delivery month of a given futures contract and simultaneous sale of another delivery month of the same commodity on the same exchange. Call option. A contract that gives the buyer of the option the right, but not the obligation, to take delivery of the underlying security at a specific price within a certain time. Canceling order. An order that deletes a customer’s previous order. Candlestick charts. A charting method, originally from Japan, in which the high and low are plotted as a single line and are referred to as shadows. Cap. A supply contract between a buyer and seller, whereby the buyer is assured that he or she will not have to pay more than a given maximum price. This type of contract is analogous to a call option. Carbon dioxide (CO2). A colorless, odorless, non-poisonous gas that is a normal part of air. Carbon dioxide is exhaled by humans and animals and is absorbed by greengrowing plants/organisms and by the sea. Carbon dioxide is a product of fossil-fuel combustion as well as other processes. It is considered a greenhouse gas as it traps heat (infrared energy) radiated by the Earth into the atmosphere and thereby contributes to the potential for global warming. The global warming potential (GWP) of other greenhouse gases is measured in relation to that of carbon dioxide, which by international scientific convention is assigned a value of one (1).

Glossary

429

Carbon dioxide equivalent. The amount of carbon dioxide by weight emitted into the atmosphere that would produce the same estimated radiative forcing as a given weight of another radiatively active gas. Carbon dioxide equivalents are computed by multiplying the weight of the gas being measured (for example, methane) by its estimated global warming potential (which is 21 for methane). “Carbon equivalent units” are defined as carbon dioxide equivalents multiplied by the carbon content of carbon dioxide (i.e. 12/44). Carbon intensity. The amount of carbon by weight emitted per unit of energy consumed. A common measure of carbon intensity is weight of carbon per British thermal unit (BTU) of energy. Carbon monoxide (CO). A colorless, odorless, highly poisonous gas made up of carbon and oxygen molecules formed by the incomplete combustion of carbon or carbonaceous material, including gasoline. It is a major air pollutant on the basis of weight. Carbon offsets. Most commonly used in reference to the output of carbon sequestration projects in the forestry sector or the output of any climate change mitigation project. Carbon sequestration. The fixation of atmospheric carbon dioxide in a carbon sink through biological or physical processes. Carbon sink. A reservoir that absorbs or takes up released carbon from another part of the carbon cycle. The four sinks, which are regions of the Earth within which carbon behaves in a systematic manner, are the atmosphere, terrestrial biosphere (usually including freshwater systems), oceans, and sediments (including fossil fuels). Carrying charge. For physical commodities such as grains and metals, the cost of storage space, insurance, and finance charges incurred by holding a physical commodity. Also referred to as cost of carry or carry. Carryover. Grain and oilseed commodities not consumed during the marketing year and remaining in storage at year’s end. These stocks are “carried over” into the next marketing year and added to the stocks produced during that crop year. Cash commodity. An actual physical commodity someone is buying or selling, for example, soybeans, corn, gold, silver, Treasury bonds, and others. Also referred to as actuals. Cash contract. A sales agreement for either immediate or future delivery of the actual product. Cash market. A place where people buy and sell the actual commodities, i.e. grain elevator, bank, etc. Spot usually refers to a cash market price for a physical commodity that is available for immediate delivery. A forward contract is a cash contract in which a seller agrees to deliver a specific cash commodity to a buyer sometime in the future. Forward contracts, in contrast to futures contracts, are privately negotiated and are not standardized. Cash settlement. Transactions generally involving index-based futures contracts that are settled in cash based on the actual value of the index on the last trading day, in contrast to those that specify the delivery of a commodity or financial instrument. CBOT. Chicago Board of Trade. Certified emission reductions (CERs). Technical term for the output of CDM projects, as defined by the Kyoto Protocol.

430

Glossary

CFD. Contract for differences. A type of crude oil swap. Chains. Serve as a designation for the strings of transactions assembled to settle a period’s business in unregulated paper commodities like Russian gas oil. Charterer. The party who contracts for use of a ship. He can do so for a voyage (a spot charter) or a period (a time charter). Charting. The use of charts to analyze market behavior and anticipate future price movements. Those who use charting as a trading method plot such factors as high, low, and settlement prices; average price movements; volume; and open interest. Charts. A display or picture of a security that plots price and/or volume (the number of shares sold). The chart is the foundation of technical analysis, and over the years, many different types of charts have been developed. C.I.F. (cost, insurance, and freight). A sales transaction in which the seller pays for the transportation and insurance of the goods up to the port of destination specified by the buyer. Clean development mechanism (CDM). Established under Article 12 of the Kyoto Protocol; goals of the CDM are to promote sustainable development in developing countries while allowing industrialized countries to earn emissions reduction credits from their investments in GHG-reduction projects in developing countries. Clearing corporation. An independent corporation that settles all trades made at the Chicago Board of Trade acting as a guarantor for all trades cleared by it; reconciles all clearing member firm accounts each day to ensure that all gains have been credited and all losses have been collected and sets and adjusts clearing member firm margins for changing market conditions. Clearing member. A member of an exchange clearinghouse. Memberships in clearing organizations are usually held by companies. Clearing members are responsible for the financial commitments of customers that clear through their firm. Clearinghouse. An agency or separate corporation of a futures exchange that is responsible for settling trading accounts, clearing trades, collecting and maintaining margin monies, regulating delivery, and reporting trading data. Clearinghouses act as third parties to all futures and options contracts acting as a buyer to every clearing member seller and a seller to every clearing member buyer. Climate change. A term used to refer to all forms of climatic inconsistency, but especially to significant change from one prevailing climatic condition to another. In some cases, “climate change” has been used synonymously with the term “global warming”; scientists, however, tend to use the term in a wider sense inclusive of natural changes in climate, including climatic cooling. See Global warming. Closed-end funds. A mutual fund that does not sell unlimited shares; one with a specific number of outstanding shares. Closing price. The last price paid for a commodity on any trading day. The exchange clearinghouse determines a firm’s net gains or losses, margin requirements, and the next day’s price limits based on each futures and options contract settlement price. CME. Chicago Mercantile Exchange.

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431

Coal. A readily combustible black or brownish-black rock whose composition, including inherent moisture, consists of more than 50% by weight and more than 70% by volume of carbonaceous material. Cogeneration. The production of electrical energy and another form of useful energy (such as heat or steam) through the sequential use of energy. Coke (petroleum). A residue high in carbon content and low in hydrogen that is the final product of thermal decomposition in the condensation process in cracking. Colonial pipeline. The on-land pipeline system connecting US Gulf Coast refineries to southeast and Atlantic Coast markets. The main artery runs from Deer Park, Texas, to Linden, New Jersey. Collar. Often structured as a zero-cost collar. A supply contract between a buyer and a seller of a commodity, whereby the buyer is assured that he will not have to pay more than some maximum price, and whereby the seller is assured of receiving some minimum price. Frequently, this takes the form of an options collar, involving the simultaneous purchase of an out-of-the-money call and sale of an out-of-the-money put. Combined cycle. An electric generating technology in which electricity is produced from otherwise lost waste heat exiting from one or more gas (combustion) turbines. Commodity. An article of commerce or a product that can be used for commerce. In a narrow sense, products traded on an authorized commodity exchange. The types of commodities include agricultural products, metals, petroleum, foreign currencies, and financial instruments and indexes, to name a few. Commodity Exchange Act (CEA). of futures trading.

The federal act that provides for federal regulation

Commodity future. A futures contract on a commodity. Unlike financial futures, the prices of commodity futures are determined by supply and demand as well as the costof-carry of the underlying. Commodity futures can therefore either be in contango (where futures prices are higher than spot prices) or backwardation (where futures are lower than spot). Commodity swap. Commodity swaps enable both producers and consumers to hedge commodity prices. The consumer is usually a fixed payer, and the producer a floating payer: if the floating-rate price of the commodity is higher than the fixed price, the difference is paid by the floating payer, and vice versa. Usually, only the payment streams, not the principal, are exchanged, although physical delivery is becoming increasingly common. Commodity Futures Trading Commission (CFTC). A commission established in 1974 that oversees the commodity exchanges in the USA. Condensate. Natural gas liquids heavier than butane. The term condensates commonly covers two quite different kinds of streams: natural gasolines and heavy condensates. Conference of Parties (COP). The meeting of parties to the United Nations Framework Convention on Climate Change. Contango. A futures market in which prices in succeeding delivery months are progressively higher. See Backwardation.

432

Glossary

Contingent swap. A swap which is only activated when rates reach a certain level or a specific event occurs. For example, drop-lock swaps only activate if rates or prices drop to a certain level or if a specified level over a benchmark is achieved. Continuation chart. A chart in which the price scale for the data for the end of a given contract and the data for the beginning of the next contract are merged in order to ease the transition of one contract to the next. Contract. An agreement as in options in which rights are exchanged by law. Correlation coefficient. When two random variables X and Y tend to vary together. The measurement is given by the ratio of the covariance of X and Y to the square root of the product of the variance of X and the variance of Y. Contracts for differences (CFDs). Long-term UK electricity swaps agreed bilaterally, generally between generators and electricity supply companies, and referenced to prices in the Electricity Pool for England and Wales. The UK government announced in late 1998 that it planned to replace the Pool system with a three-tier market. A short-dated swap agreement used to minimize the basis risk between the daily published Platt’s quote for dated or physical Brent in a specific time window in the future and the forward price quote for a specific month (15-day market). Conventional thermal electricity generation. Electricity generated by an electric power plant using coal, petroleum, or gas as its source of energy. Convergence. A term referring to cash and futures prices tending to come together (i.e. the basis approaches zero) as the futures contract nears expiration. Cost of carry (or carry). For physical commodities such as grains and metals, the cost of storage space, insurance, and finance charges incurred by holding a physical commodity. In interest rate futures markets, it refers to the differential between the yield on a cash instrument and the cost of funds necessary to buy the instrument. Cost basis. The cost of a given share or group of stock shares. Covariance. Multiplies the deviation of each variable from its mean, adds those products, and then divides by the number of observations. Cover. Purchasing back a contract sold earlier. Covered option. A covered call option is one where the writer owns the underlying asset on which the option is written. Generally, a covered call would only be written if the writer believed volatility to be overpriced in the market; the lower the volatility, the less premium the writer gains in return for giving up their upside in the underlying. Crack spreads. The spread between crude oil and its products: heating oil and unleaded gasoline play a major role in the trading process. Cross-hedging. Hedging a cash commodity using a different but related futures contract when there is no futures contract for the cash commodity being hedged and the cash and futures markets follow similar price trends (e.g. using soybean meal futures to hedge fish meal). Crude oil. A full-ranging hydrocarbon mixture produced from a reservoir after any associated gas has been removed. Among the most commonly traded crudes are the North Sea’s Brent Blend, the US’s West Texas Intermediate (WTI), and Dubai. Cubic foot (cf), natural gas. The amount of natural gas contained at standard temperature and pressure (60º Fahrenheit and 14.73 pounds standard per square inch) in a cube whose edges are one foot long.

Glossary

433

Customer margin. Within the futures industry, financial guarantees required of both buyers and sellers of futures contracts and sellers of options contracts to ensure fulfilling of contract obligations.

D Daily trading limit. The maximum price range set by the exchange cash day for a contract. Day order. An order that if not executed expires automatically at the end of the trading session on the day it was entered. Day traders. Speculators who take positions in futures or options contracts and liquidate them prior to the close of the same trading day. Deep-in-the-money. A deep-in-the-money call option has the strike price of the option well below the current price of the underlying instrument. A deep-in-the-money put option has the strike price of the option well above the current price of the underlying instrument. Default. The failure to perform on a futures contract as required by exchange rules, such as a failure to meet a margin call or to make or take delivery. Deferred (delivery) month. The more distant month(s) in which futures trading is taking place, as distinguished from the nearby (delivery) month. Delivery. The transfer of the cash commodity from the seller of a futures contract to the buyer of a futures contract. Each futures exchange has specific procedures for delivery of a cash commodity. Some futures contracts, such as stock index contracts, are cash settled. Delivery points. The locations and facilities designated by a futures exchange where stocks of a commodity may be delivered in fulfillment of a futures contract, under procedures established by the exchange. Delta. The amount by which the price of an option changes for every dollar move in the underlying instrument. Delta-hedged. An options strategy that protects an option against small price changes in the option’s underlying instrument. These hedges are constructed by taking a position in the underlying instrument that is equal in magnitude but opposite in sign (+/–) to the option’s delta. Delta neutral. This is an “options/options” or “options/underlying instrument” position constructed so that it is relatively insensitive to the price movement of the underlying instruments. This is arranged by selecting a calculated ratio of offsetting short and long positions. Demand-side management (DSM). Demand-side management (DSM) programs consist of the planning, implementing, and monitoring activities of electric utilities, which are designed to encourage consumers to modify their level and pattern of electricity usage. Derivative. A financial instrument, traded on or off an exchange, the price of which is directly dependent upon the value of one or more underlying securities, equity indices, debt instruments, commodities, other derivative instruments, or any agreed upon pricing index or arrangement. Derivatives involve the trading of rights or obligations based on the underlying product but do not directly transfer property. They are used to hedge risk or to exchange a floating rate of return for a fixed rate of return.

434

Glossary

Differentials. Price differences between classes, grades, and delivery locations of various stocks of the same commodity. Direct load control. DSM program activities that can interrupt consumer load at the time of annual peak load, by direct control of the utility system operator, by interrupting or decreasing power supply to individual appliances/equipment on consumer premises. This type of control usually involves residential consumers. Direct load control as defined here excludes interruptible load and other load management effects. Discounting. Reducing future costs and benefits to reflect the time value of money and the common preference of consumption now rather than later. Distributed generation. A distributed generation system involves small generation capacities located on a utility’s distribution system for the purpose of meeting local (substation level) peak loads and/or displacing the need to build additional (or upgrade) local distribution lines. Divergence. When two or more averages or indices fail to show confirming trends. Double bottom (top). The price action of a security or market average where it has declined (advanced) two times to the same approximate level, indicating the existence of a support (resistance) level and a possibility that the downward (upward) trend has ended. Double top. A price pattern seen on a chart. The pattern occurs when prices rise to a resistance level on significant volume, retreat to a support level, and subsequently return to the resistance level on decreased volume. Prices then decline and break through the support level, marking the beginning of a new downtrend in the price of the stock. Downside risk. A long forward position taken without an offsetting short physical position in the underlying commodity is said to have downside risk. It means that the trader is speculating that the price of the commodity will increase. See Upside risk. Downstream. A relative term which indicates greater removal from origins than some point of reference. For example, a petrochemical plant which cracks naphtha lies downstream from a refinery. Money made by marketing products constitutes downstream profits compared to earnings on crude sales. See Upstream. Dual trading. Dual trading occurs when (a) a floor broker executes customer orders and, on the same day, trades for his own account or an account in which he has an interest; or (b) a futures commission merchant carries customer accounts and also trades, or permits its employees to trade, in accounts in which it has a proprietary interest, also on the same day.

E Early crediting. Allowing crediting of emissions reduction achieved prior to the start of a legally imposed emission control period. These credits can then be used towards compliance, once a regulatory system is in place. Electronic order. An order placed electronically (without the use of a broker) either via the Internet or an electronic trading system. Electronic trading systems. Systems that allow participating exchanges to list their products for trading after the close of the exchange’s open outcry trading hours (i.e. Chicago Board of Trade’s Project A, Chicago Mercantile Exchange’s GLOBEX, and New York Mercantile Exchange’s ACCESS).

Glossary

435

Emissions. Anthropogenic release of gases to the atmosphere. In the context of global climate change, they consist of radiatively important greenhouse gases (e.g. the release of carbon dioxide during fuel combustion). See Greenhouse gases. Emissions reduction units (ERUs). Technical term for the output of JI projects, as defined by the Kyoto Protocol. Emission scenario. A plausible representation of future emissions of greenhouse gases and aerosols, based on a consistent set of assumptions about driving forces. Emissions trading. Emissions trading is a regulatory program that allows companies the flexibility to select cost-effective solutions to achieve established environmental goals. With emissions trading, companies can meet established emissions goals by (a) reducing emissions from a discrete emissions unit; (b) reducing emissions from another place within the facility; or (c) securing emission reductions from another facility. Emissions trading encourages compliance and financial managers to pursue costeffective emissions reduction strategies and provides incentives to emitting entrepreneurs to develop the means by which emissions can inexpensively be reduced. Energy demand. The requirement for energy as an input to provide products and/or services. Energy efficiency (EE). Demand-side management programs that are aimed at reducing the energy used by specific end-use devices and systems, typically without affecting the services provided. These programs reduce overall electricity consumption. Examples include energy-saving appliances and lighting programs; high-efficiency heating, ventilating, and air-conditioning systems or control modifications; and energyefficient buildings. Enhanced recovery. Techniques used to increase or stretch over time the production of wells. Equity. The value of a futures trading account if all open positions were offset at the current market price. Exchange for physicals. A transaction generally used by two hedgers who want to exchange futures for cash positions. Also referred to as “against actuals” or “versus cash.” Exchange-traded funds (ETFs). Collections of stocks that are bought and sold as a package on an exchange, principally the American Stock Exchange (AMEX), but also the New York Stock Exchange (NYSE) and the Chicago Board Options Exchange (CBOE). Exercise. The process by which the holder of an option makes or receives delivery of futures contracts of the underlying futures market. Exercise price. The price at which the futures contract underlying a call or put option can be purchased (if a call) or sold (if a put). Also referred to as strike price. Exit. The point at which a trader closes out of a trade. Expiration. The last day on which an option can be traded. Expiration date. Options on futures generally expire on a specific date during the month preceding the futures contract delivery month. For example, an option on a March futures contract expires in February but is referred to as a March option because its exercise would result in a March futures contract position.

436

Glossary

F FAS 133. The standard for financial reporting of derivatives and hedging transactions; adopted in 1998 by the Financial Accounting Standards Board to resolve inconsistent previous reporting standards and practices. It went into effect at most US companies at the beginning of 2001. Federal Energy Regulatory Commission (FERC). Considered an independent regulatory agency responsible primarily to Congress but housed in the Department of Energy. Feedstock. Material used in a processing plant. Float. The number of shares currently available for trading. Floor broker (FB). An individual who executes orders for the purchase or sale of any commodity futures or options contract on any contract market for any other person. Floor trader (FT). An individual who executes trades for the purchase or sale of any commodity futures or options contract on any contract market for such individual’s own account. F.O.B. (free on board). A sales transaction in which the seller makes the product available for pick up at a specified port or terminal at a specified price, and the buyer pays for the subsequent transportation and insurance. Forex market. An over-the-counter market where buyers and sellers conduct foreign exchange business by telephone and other means of communication. Also referred to as a foreign exchange market. Forward (cash) contract. A cash contract in which a seller agrees to deliver a specific cash commodity to a buyer sometime in the future. Forward contracts, in contrast to futures contracts, are privately negotiated and are not standardized. Forward curve. Represents the price at which buyers and sellers purchase and sell allowances in forward settling transactions. Forward market prices decrease more than five years beyond the spot vintage due to regulatory uncertainty. Forward-rate agreements (FRAs). Cash payments are made daily as the spot rate varies above or below an agreed-upon forward rate and can be hedged with Eurodollar futures. Fossil fuel. A general term for buried combustible geologic deposits of organic materials, formed from decayed plants and animals, that have been converted to crude oil, coal, natural gas, or heavy oils by exposure to heat and pressure in the Earth’s crust over hundreds of millions of years. Frequency distribution. A chart showing the number of times (or “frequency”) an event occurs for each possible value of the event. The vertical or y-axis of the chart is the frequency axis, and the horizontal or x-axis shows the different values the variable being measured can take. Front-loaded. Commission and fees taken out of investment capital before the money is put to work. Front month. The first expiration month in a series of months. Front-running. The practice of trading ahead of large orders to take advantage of favorable price movements. Brokers are prohibited from this practice.

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437

Fundamental analysis. The analytical method by which only the sales, earnings, and the value of a given tradable’s assets may be considered. Fundamentals. The theory that holds that stock market activity may be predicted by looking at the relative data and statistics of a stock as well as the management of the company in question and its earnings. Future volatility. A prediction of what volatility may be like in the future. Futures commission merchant (FCM). An individual or organization that solicits or accepts orders to buy or sell futures contracts or options on futures and accepts money or other assets from customers to support such orders. Also referred to as “commission house” or “wire house.” Futures contract. A legally binding agreement, made on the trading floor of a futures exchange, to buy or sell a commodity or financial instrument sometime in the future. Futures contracts are standardized according to the quality, quantity, and delivery time and location for each commodity. The only variable is price, which is discovered on an exchange trading floor. Futures exchange. A central marketplace with established rules and regulations where buyers and sellers meet to trade futures and options on futures contracts. Futures market. A trade center for quoting prices on contracts for the delivery of a specified quantity of a commodity at a specified time and place in the future.

G Gamma. The degree by which the delta changes with respect to changes in the underlying instrument’s price. See Delta. Gap. A day in which the daily range is completely above or below the previous day’s daily range. Gas oil. European and Asian designation for No. 2 heating oil and No. 2 diesel fuel. Gas to liquids (GTLs). A process that combines the carbon and hydrogen elements in natural gas molecules to make synthetic liquid petroleum products, such as diesel fuel. Global warming. An increase in the near-surface temperature of the Earth. Global warming has occurred in the distant past as the result of natural influences, but the term is today most often used to refer to the warming some scientists predict will occur as a result of increased anthropogenic emissions of greenhouse gases. See Climate change. Greeks. Jargon; a loose term encapsulating the set of risk variables used by options traders. Greenhouse gases (GHGs). Some greenhouse gases occur naturally, trapping heat in the atmosphere (such as water vapor, carbon dioxide, methane, nitrous oxide, and ozone), and their atmospheric levels are increased by human activity. The Kyoto Protocol addresses the control of atmospheric levels of six GHGs: carbon dioxide, methane, nitrous oxide, hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). Grid. The layout of an electrical distribution system.

438

Glossary

H Head and shoulders. When the middle price peak of a given tradable is higher than those around it. Heat content of a quantity of fuel, net. The amount of usable heat energy released when a fuel is burned under conditions similar to those in which it is normally used. Net heat content is also referred to as the lower heating value. BTU conversion factors typically used by the Energy Information Administration represent gross heat content. Heavy crude oil. Has API gravity lower than 28 degrees. The lower the API gravity, the heavier the oil. Hedge fund. A mutual fund involving speculative investing in futures, swaps, and options. Hedger. An individual or company owning or planning to own a cash commodity corn, soybeans, wheat, US Treasury bonds, notes, bills, etc. and concerned that the cost of the commodity may change before either buying or selling it in the cash market. A hedger achieves protection against changing cash prices by purchasing (selling) futures contracts of the same or similar commodity and later offsetting that position by selling (purchasing) futures contracts of the same quantity and type as the initial transaction. Hedging. The practice of offsetting the price risk inherent in any cash market position by taking an equal but opposite position in the futures market. Hedgers use the futures markets to protect their business from adverse price changes. High. The highest price of the day for a particular futures contract. Historic volatility. How much contract price has fluctuated over a period of time in the past; usually calculated by taking a standard deviation of price changes over a time period. Hydrocarbon. An organic compound containing only carbon and hydrogen. Hydrocarbons are what comprise petroleum products, natural gas, and coal.

I Implied volatility. The volatility computed using the actual market prices of an option contract and one of a number of pricing models. For example, if the market price of an option rises without a change in the price of the underlying stock or future, implied volatility will have risen. Independent introducing broker. A firm or individual that solicits and accepts commodity futures orders from customers but does not accept money, securities, or property from the customer. Unlike a guaranteed introducing broker, an independent introducing broker is subject to minimum capital requirements and can introduce accounts to any registered Futures Commission Merchant. Intergovernmental Panel on Climate Change (IPCC). The IPCC was established in 1988 by the World Meteorological Organization and the United Nations Environment Program. It surveys worldwide technical and scientific literature, publishing assessment reports widely accepted as the most credible source on climate change.

Glossary

439

International Petroleum Exchange. London oil exchange that has futures and options contracts in Brent Blend crude oil and gas oil and a futures contract in UK natural gas. The IPE and the New York Mercantile Exchange announced merger discussions in November 1998 (www.theipe.com). ISDA Master Agreement. The International Swaps and Derivatives Association (ISDA) over-the-counter derivatives master agreement was drawn up by the New York–based trade association in 1987 and revised in 1992. The agreement is commonly used for contracts in various energy derivatives markets, especially the US gas market (www.isda.org). Intercommodity spread. The purchase of a given delivery month of one futures market and the simultaneous sale of the same delivery month of a different, but related, futures market. Intrinsic value. The portion of an option’s premium that is represented when the cash market price is greater than the exercise price; a known constant equal to the difference between the strike price and the underlying market price.

J Jet fuel. A refined petroleum product used in jet aircraft engines. It includes kerosenetype jet fuel and naphtha-type jet fuel. Joint implementation (JI). A bilateral agreement between two entities to complete a GHG mitigation project. The investor is an industrialized nation required to reduce emissions under the United Nations Framework Convention on Climate Change. JI may be able to provide credit for emissions abatement to the investor at a lower cost than domestic abatement.

K Kyoto Protocol. An international agreement struck by 159 nations attending the Third Conference of Parties (COP) to the United Nations Framework Convention on Climate Change (held in December of 1997 in Kyoto, Japan) to reduce worldwide emissions of greenhouse gases. On February 16, 2005, this treaty entered into effect, binding 35 industrialized countries to cut GHG emissions to an average of 5% below 1990 levels.

L Last trading day. The last day on which trading may occur in a given futures or option. Leg. One side of a spread. Leg out. In rolling forward in futures, a method that would result in liquidating a position. Leverage. The ability to control large dollar amounts of a commodity with a comparatively small amount of capital. Lifting. Tankers and barges loading petroleum at a terminal or transfer point. Lignite. The lowest rank of coal, often referred to as brown coal. Limit order. An order to buy or sell when a price is fixed.

440

Glossary

Limit up, limit down. Commodity exchange restrictions on the maximum upward or downward movements permitted in the price for a commodity during any trading session day. Limits. The maximum number of speculative futures contracts one can hold as determined by the Commodity Futures Trading Commission and/or the exchange upon which the contract is traded. Also referred to as trading limit. The maximum advance or decline from the previous day’s settlement permitted for a contract in one trading session by the rules of the exchange. Linkage. The ability to buy (sell) contracts on one exchange and later sell (buy) them on another exchange. Liquefied natural gas (LNG). Natural gas (primarily methane) that has been liquefied by reducing its temperature to –260º Fahrenheit at atmospheric pressure. (The volume of the LNG is 1/600 that of the gas in its vapor state.) Liquidate. Selling (or purchasing) futures contracts of the same delivery month purchased (or sold) during an earlier transaction or making (or taking) delivery of the cash commodity represented by the futures contract. Liquidity (liquid market). A characteristic of a security or commodity market with enough units outstanding to allow large transactions without a substantial change in price. Load management. Steps taken to reduce power demand at peak load times or to shift some of it to off-peak periods. This may be with reference to peak hours, peak days, or peak seasons. Air-conditioning usage, the primary element affecting electric peaks, is the prime target for load management efforts. Local. The trader in a pit of a commodity exchange who buys and sells for his or her account. Long. Establishing ownership of the responsibilities of a buyer of a tradable; holding securities in anticipation of a price increase in that security. Long hedge. Buying futures contracts to protect against a possible price increase of cash commodities that will be purchased in the future. At the time the cash commodities are bought, the open futures position is closed by selling an equal number and type of futures contracts as those that were initially purchased. Also referred to as a buying hedge. Low. The lowest price of the day for a particular futures contract.

M Margin call. A call from a clearinghouse to a clearing member, or from a brokerage firm to a customer, to bring margin deposits up to a required minimum level. Marked to market. At the end of each business day the open positions carried in an account held at a brokerage firm are credited or debited funds based on the settlement price of the open positions that day. Market-based pricing. Prices of electric power or other forms of energy determined in an open market system of supply and demand under which prices are set solely by agreements as to what buyers will pay and sellers will accept. Market maker. A broker or bank continually prepared to make a two-way price to purchase or sell for a futures, options, or swaps contract.

Glossary

441

Market risk. The uncertainty of returns attributable to fluctuation of the entire market. Market sentiment. Crowd psychology, typically a measurement of bullish or bearish attitudes among investors and traders. Market timing. Using analytical tools to devise entry and exit methods. Mean. When the sum of the values is divided by the number of observations, that is, Mean of Platts Singapore (MOPS), where traders take the high value and low value to create the MOPS reference price, against which most over-the-counter swaps derivatives and physical oil markets are priced. Mean reverting. The term adopted in academic literature for one possible state of a price series: that state when price is oscillating randomly about some (unknown) mean value; that is, it is not trending. Momentum. A time series representing change of today’s price from some fixed number of days back in history. Montreal Protocol on Substances that Deplete the Ozone Layer. This protocol was adopted in Montreal in 1987. It was adjusted in London (1990), Copenhagen (1992), Vienna (1995), Montreal (1997), and Beijing (1999). The protocol controls the consumption and production of chemicals containing bromine or chlorine that destroy stratospheric ozone. Moving average. A mathematical procedure to smooth or eliminate the fluctuations in data and to assist in determining when to buy and sell. Moving averages emphasize the direction of a trend, confirm trend reversals, and smooth out price and volume fluctuations or “noise” that can confuse interpretation of the market; the sum of a value plus a selected number of previous values divided by the total number of values.

N Naphtha. A generic term applied to a petroleum fraction with an approximate boiling range between 122º and 400º Fahrenheit. Blended further or mixed with other materials, it makes high-grade motor gasoline or jet fuel. Also used as a solvent, petrochemical feedstock, or as raw material for the production of town gas. Naphthenic naphtha. Usually favored as reformer feedstock. National Environmental Policy Act (NEPA). The environmental law that establishes federal energy policy, sets goals, and provides means for carrying out the policy. It is a national policy for the purpose of encouraging productive and enjoyable harmony between human beings and the environment. It is responsible for promoting efforts that will prevent or eliminate damage to the environment and biosphere and for establishing a Council on Environmental Quality. National Futures Association (NFA). An industry-wide, industry-supported, selfregulatory organization for futures and options markets. Natural gas liquids (NGL). A general term for all liquid products separated from natural gas in gas processing or cycling plants. They include natural gas plant liquids and lease condensate. Natural gas storage. Use of a depleted formation (or well) near a market to store gas brought in from another field or location.

442

Glossary

Negawatt. A megawatt of power avoided or saved from use on the energy grid. Nitrogen oxides (NOx). A major air pollutant produced by agricultural and industrial activities and the burning of fossil fuels and solid wastes. Nitrogen oxides can contribute to the formation of photochemical ozone (smog) and impair visibility and have proven health consequences. Nonattainment area. Any area that does not meet the national primary or secondary ambient air quality standard established by the US Environmental Protection Agency for designated pollutants, such as carbon monoxide and ozone. Normalized. Adjusting a time series so that the series lies in a prescribed normal, standard range. Notice day. The day that a notice of intent to deliver is issued to a futures contract holder. NYMEX. New York Mercantile Exchange. The world’s largest energy futures exchange (www.nymex.com).

O Offer. An expression indicating one’s desire to sell a commodity at a given price. See Bid. Offset. Taking a second futures or options position opposite to the initial or opening position. Selling (or purchasing) futures contracts of the same delivery month purchased (or sold) during an earlier transaction or making (or taking) delivery of the cash commodity represented by the futures contract. Open. The period at the beginning of the trading session officially designated by the exchange during which all transactions are considered made “at the open.” Open interest. The total number of futures or options contracts of a given commodity that have not yet been offset by an opposite futures or option transaction nor fulfilled by delivery of the commodity or option exercise. Each open transaction has a buyer and a seller, but for calculation of open interest, only one side of the contract is counted. Open outcry. Method of public auction for making verbal bids and offers in the trading pits or rings of futures exchanges. Opportunity costs. Income foregone by the commitment of resources to another use. Option. A contract that provides the right, but not the obligation, to buy or sell a specified amount of a security within a specified time period. Option buyer. The purchaser of either a call or put option. Option buyers receive the right, but not the obligation, to assume a futures position. Also referred to as the holder. Option contract. A contract that gives the buyer the right, but not the obligation, to buy or sell a specified quantity of a commodity or a futures contract at a specific price within a specified period of time. The seller of the option has the obligation to sell the commodity or futures contract or buy it from the option buyer at the exercise price if the option is exercised. Option premium. The price of an option and the sum of money that the option buyer pays and the option seller receives for the rights granted by the option. Option seller. The person who sells an option in return for a premium and is obligated to perform when the holder exercises his right under the option contract. Also referred to as the writer. See Grantor.

Glossary

443

Option spread. The simultaneous purchase and sale of one or more options contracts, futures, and/or cash positions. Order. The number of days of past price history used to predict the following day’s price. Out-of-the-money. A call option whose exercise price is above the current market price of the underlying security or futures contract. For example, if a commodity price is $500, then a call option purchased for a strike price of $550 is considered out-of-the-money. Over-the-counter market (OTC). The largest derivatives market in energy is in the over-the-counter market; the majority of these contracts are traded bilaterally between companies. Also, the majority of trades are conducted under a bilateral ISDA Master Agreement. These OTC Trades, as they are known, can be cleared via the New York Mercantile Exchange and the Intercontinental Exchange (www.isda.org; www.nymex.com; www.theice.com).

P Peak load. The highest electrical demand within a particular period of time. Daily electric peaks on weekdays occur in the late afternoon and early evening, usually between 4 to 7 P.M. in the winter and 12 to 8 P.M. in the summer. Annual peaks occur on hot summer days. Pit. The area on the trading floor where futures and options on futures contracts are bought and sold. Pits are usually raised octagonal platforms with steps descending on the inside that permit buyers and sellers of contracts to see each other. Position. A market commitment. A buyer of a futures contract is said to have a long position, and conversely, a seller of futures contracts is said to have a short position. Position limit. The maximum number of speculative futures contracts one can hold as determined by the Commodity Futures Trading Commission and/or the exchange upon which the contract is traded. Also referred to as trading limit. Position trader. An approach to trading in which the trader either buys or sells contracts and holds them for an extended period of time. Premium. Refers to (a) the amount a price would be increased to purchase a better quality commodity; (b) a futures delivery month selling at a higher price than another; (c) cash prices that are above the futures price; (d) the price paid by the buyer of an option; or (e) the price received by the seller of an option. Price limit. The maximum advance or decline from the previous day’s settlement permitted for a contract in one trading session by the rules of the exchange. According to the Chicago Board of Trade rules, an expanded allowable price range set during volatile markets. Production sharing agreement. Contract in use in African, Middle Eastern, Far Eastern, and Latin American countries regulating relationships between the state and oil companies with regards to the exploration and production of hydrocarbons. Program trading. Trades based on signals from computer programs, usually entered directly from the trader’s computer to the market’s computer system. Prototype Carbon Fund. Established in 1999 by the World Bank, the fund pilots production of emissions reduction within the framework of Joint Implementation (JI) and the Clean Development Mechanism (CDM). The fund invests contributions made by companies and governments in projects designed to reduce emissions as consistent with the Kyoto Protocol.

444

Glossary

Purchasing hedge (or long hedge). Buying futures contracts to protect against a possible price increase of cash commodities that will be purchased in the future. At the time the cash commodities are bought, the open futures position is closed by selling an equal number and type of futures contracts as those that were initially purchased. Also referred to as a buying hedge. The practice of offsetting the price risk inherent in any cash market position by taking an equal but opposite position in the futures market. Hedgers use the futures markets to protect their business from adverse price changes. Put option. A contract to sell a specified amount of a stock or commodity at an agreed time at the stated exercise price.

R Range. The difference between the high and low price during a given period. Ratio. The relation that one quantity bears to another of the same kind, with respect to magnitude or numerical value. Realized/unrealized P/L. The difference between trading revenues that are generated on positions that have been offset and closed versus those associated with the marking of open positions to current market prices. Reference price. In an energy derivatives contract, the settlement price of the contract based on a particular location or particular blend of the commodity. Refiner acquisition cost of crude oil. The cost of crude oil, including transportation and other fees, paid by the refiner. The composite cost is the weighted average of domestic and imported crude oil costs. See US refiner acquisition cost of imported crude oil. Refinery output (petroleum). The total amount of petroleum products produced at a refinery. Includes petroleum consumed by the refinery. Refinery processing gain (petroleum). The amount by which the total volume of refinery output is greater than the total volume of refinery input for a given period of time. Refinery processing loss (petroleum). The amount by which the total volume of refinery output is less than the total volume of refinery input for a given period of time. Reformulated gasoline. Finished motor gasoline formulated for use in motor vehicles, the composition and properties of which meet requirements of the reformulated gasoline regulations promulgated by the US Environmental Protection Agency under Section 211(k) of the Clean Air Act. Regression (simple). A mathematical way of stating the statistical linear relationship between one independent and one dependent variable. Relative strength index (RSI). An indicator invented by J. Welles Wilder and used to ascertain overbought/oversold and divergent situations. Renewable energy credits (RECs). Also known as “green tags” or “tradable renewable certificates.” These credits are traded in both the regulatory and voluntary markets. The voluntary REC market is driven by demand for green energy. Renewable energy resources. Energy resources that are naturally replenishing but flow-limited. They are virtually inexhaustible in duration but limited in the amount of energy that is available per unit of time. Renewable energy resources include biomass, hydro, geothermal, solar, wind, ocean thermal, wave action, and tidal action.

Glossary

445

Renewable portfolio standards (RPS). Regulations mandating that a certain amount of a state’s electricity must be derived from generation using such resources as wind, solar, biomass and tidal energy, and methane from waste. Reportable positions. The number of open contracts specified by the CFTC when a firm or individual must begin reporting total positions by delivery month to the authorized exchange and/or the CFTC. Reserve requirements. The minimum amount of cash and liquid assets as a percentage of demand deposits and time deposits that member banks of the Federal Reserve are required to maintain. Residual fuel oil. A general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after the distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Resistance. A price level at which rising prices have stopped rising and either moved sideways or reversed direction; usually seen as a price chart pattern. Retracement. A price movement in the opposite direction of the previous trend. Reversal gap. A chart formation where the low of the last day is completely above the previous day’s range, with the close above midrange and above the open. Risk (implied). In which the formula produces the percentage overbought/oversold for a contract using the price, a moving average, and the option’s implied volatility. Risk management. Control and limitation of the risks faced by an organization due to its exposure to changes in financial market variables, such as foreign exchange and interest rates, equity and commodity prices, or counterparty creditworthiness. Risk measurement. Assessment of a firm’s exposure to risk. Roll. Substituting a far option for a near option on the same underlying instrument at the same strike price; also to roll forward or roll over. Roll-over risk. The risk that a derivative hedge position will be at a loss at expiry, necessitating a cash payment when the expiring hedge is replaced with a new one. Round turn. A completed futures transaction involving both a purchase and a liquidating sale or a sale followed by a covering purchase.

S Seasonal trend. A consistent but short-lived rise or drop in market activity that occurs due to predictable changes in climate or calendar. Seasonality. All energy futures markets are affected to some extent by an annual seasonal cycle or “seasonality.” This seasonal cycle or pattern refers to the tendency of market prices to move in a given direction at certain times of the year. Sequestration. Capacity to absorb carbon dioxide out of the air through the process of photosynthesis. Securitization. The packaging of assets (normally debt of some description) into securities. These securities may be higher yielding and more freely tradable than the unpackaged assets. Securitizing production revenues has become increasingly popular with commodity producers over the last few years. Electric utilities have also started securitizing their retail revenue.

446

Glossary

Selling short. Selling a security and then borrowing the security for delivery with the intent of replacing the security at a lower price. In futures trading, selling short is to assume the responsibility of the seller versus the buyer in the establishment of the futures contract between parties. Settlement. The price at which all outstanding positions in a stock or commodity are marked to market. Typically, the closing price. Settlement price. The last price paid for a commodity on any trading day. Settlement risk. Settlement risk is the risk that arises when payments are not exchanged simultaneously. Short. (noun) One who has sold futures contracts or plans to purchase a cash commodity. (verb) Selling futures contracts or initiating a cash forward contract sale without offsetting a particular market position. Simex (Singapore Monetary Exchange). Now called Singapore Exchange (SGX). Simple moving average. The arithmetic mean or average of a series of prices over a period of time. The longer the period of time studied (that is, the larger the denominator of the average), the less impact an individual data point has on the average. Slippage. The difference between estimated transaction costs and actual transaction costs. Sleeving. A transaction whereby a counterparty, which does not have credit with another counterparty, asks a third party that has credit with both parties to be a middle person to facilitate a trade. SO2 allowance trading. Allowance trading is the centerpiece of the Washington, DC–based Environmental Protection Agency’s (EPA) Acid Rain Program. Sour/sweet crude. Definitions which describe the degree of a given crude’s sulfur content. Sour refers to high sulfur and sweet to low sulfur. Spark spread. The difference between the price of electricity sold by a generator and the price of the fuel used to generate it, adjusted for equivalent units. The spark spread can be expressed in $/mWh or $/mmBm (or other applicable units). Speculator. A market participant who tries to profit from buying and selling futures and options contracts by anticipating future price movements. Speculators assume market price risk and add liquidity and capital to the futures markets. Spike. A sharp rise in price in a single day or two; may be as great as 15–30%, indicating the time for an immediate sale. Spot. Usually refers to a cash market price for a physical commodity that is available for immediate delivery. Spot prices. Same as cash price; the price at which a commodity is selling at a particular time and place. Spread. A trade in which two related contracts/stocks/bonds/options are traded to exploit the relative differences in price change between the two. Spreading. The simultaneous buying and selling of two related markets in the expectation that a profit will be made when the position is offset. Standard deviation. The positive square root of the expected value of the square of the difference between a random variable and its mean. A measure of the fluctuation in a stock’s monthly return over the preceding year.

Glossary

447

Stock index futures. A futures contract traded that uses a market index as the underlying instrument. Typically, the value of the contract is $500 times the underlying index. The delivery mechanism is usually cash settlement. Stocks. Inventories of fuel stored for future use. Stop-limit order. A variation of a stop order in which a trade must be executed at the exact price or better. If the order cannot be executed, it is held until the stated price or better is reached again. Stop loss. The risk management technique in which the trade is liquidated to halt any further decline in value. Stop order. An order to buy or sell when the market reaches a specified point. A stop order to buy becomes a market order when the futures contract trades (or is bid) at or above the stop price. A stop order to sell becomes a market order when the futures contract trades (or is offered) at or below the stop price. Stops. Buy stops are orders that are placed at a predetermined price over the current price of the market. Straddle. The purchase or sale of an equivalent number of puts and calls on an underlying stock with the same exercise price and expiration date. Strategic storage. Volumes required for covering lack or reduction of supplies from extra-European sources or crises in the natural gas system. Strike price. The price per unit at which the holder of an option may receive or deliver the underlying unit. Also known as the exercise price. Strips. An option strategy in which an investor buys one call and two puts on the same underlying security with the same exercise price and expiration date. Sulfur dioxide (SO2). A pungent, colorless gas formed primarily by the combustion of fossil fuels; it becomes an air pollutant when present in large amounts. It is regulated in the US. Swap. An agreement whereby a floating price is exchanged for a fixed price over a specified period. It is an off-balance-sheet financial arrangement, which involves no transfer of physical energy; both parties settle their contractual obligations by means of a transfer of cash. The agreement defines the volume, duration, and fixed reference price. Differences are settled in cash for specific periods—monthly, quarterly, or six-monthly. Swaps are also known as contracts for differences and as fixed-for-floating contracts. See ISDA. Swaption. An option to purchase (call option) or sell (put option) a swap at some future date. Sweet crude. Crude oil containing a relatively low percentage by weight of sulfur, typically less than 1%. Swing. Variations in gas demand. Swing producer. A company or country that changes its crude oil output to meet fluctuations in market demand. Saudi Arabia is seen as the world’s major swing producer as it deliberately limits its crude oil production in an attempt to keep supply and demand roughly in balance. Synthetic securities. Security created by buying and writing a combination of options that imitate the risk and profit profile of a security.

448

Glossary

T Take-or-pay. In a buyer’s contract, take-or-pay is the obligation to pay for a specified amount of gas, whether this amount is taken or not. Depending on the contract terms, under-takes or over-takes may be taken as make-up or carry forward into the next contract period. When it is credited into another contract period, this is called make-up gas. Technical analysis. Technical analysis is based on the presumption that price takes into consideration all factors that could influence the price of the commodity. It is therefore broader than fundamental analysis, which looks at supply and demand. Past price movements can be analyzed for indication of future commodity price movements. Technical rally. A short rise in commodity futures prices within a general declining trend. Such a rally may result from bargain hunting by market participants or because technical analysts have noticed a particular support level at which the commodity price is expected to increase. Theta. The measurement of the time decay of a position. Tick. The minimum fluctuation of a tradable. For example, bonds trade in 32 seconds, while most stocks trade in eighths. Time series. A collection of observations made sequentially in time and indexed by time. Time value. The amount of money options buyers are willing to pay for an option in anticipation that over time a change in the underlying futures price will cause the option to increase in value. In general, an option premium is the sum of time value and intrinsic value. Any amount by which an option premium exceeds the option’s intrinsic value can be considered time value. Trading bands. Lines plotted in and around the price structure to form an envelope, answering whether prices are high or low on a relative basis and forewarning whether to buy or sell by using indicators to confirm price action. Trading limit. The maximum number of speculative futures contracts one can hold as determined by the Commodity Futures Trading Commission and/or the exchange upon which the contract is traded. Also referred to as position limit. Trading range. The difference between the high and low prices traded during a period of time; in commodities, the high/low price limit established by the exchange for a specific commodity for any one day’s trading. Trend. The general drift, tendency, or bent of a set of statistical data as related to time. Trend-following. Moving in the direction of the prevailing price movement. Trending market. Price moves in a single direction, generally closing at an extreme for the day. Trendless. Price movement that vacillates to the degree that a clear trend cannot be identified. Trendline. A line drawn that connects either a series of highs or lows in a trend. The trendline can represent either support, as in an uptrend line, or resistance, as in a downtrend line. Consolidations are marked by horizontal trendlines.

Glossary

449

Trigger condition. The payout of path-dependent options, such as barrier options and digital options, depends on a specified market variable satisfying a specific trigger condition. The most common condition is that the spot rate (or price) of the underlying must trade through a specified level before the option becomes active (or inactive), but many other types of condition are possible.

U Uncovered option. The buy or sale of an option without a position in the underlying futures contract. Also known as a naked option. Underlying futures contract. The specific futures contract that the option conveys the right to buy (in the case of a call) or sell (in the case of a put). Underlying instrument. A trading instrument subject to purchase upon exercise. United Nations Framework Convention on Climate Change (UNFCCC). The international treaty unveiled at the United Nations Conference on Environment and Development (UNCED) in June 1992. The UNFCCC commits signatory countries to stabilize anthropogenic (i.e. human-induced) greenhouse gas emissions to “levels that would prevent dangerous anthropogenic interference with the climate system.” The UNFCCC also requires that all signatory parties develop and update national inventories of anthropogenic emissions of all greenhouse gases not otherwise controlled by the Montreal Protocol. Out of 155 countries that have ratified this accord, the USA was the first industrialized nation to do so. Upside risk. A short forward position taken without an offsetting long physical position in the underlying commodity is said to have upside risk. This means that the trader is speculating that the price of the commodity will decline. See Downside risk. Upstream. Refers to all hydrocarbon exploration and production activities. See Downstream.

V Value-at-risk (VaR). The worst loss expected to be suffered over a given period of time with a given probability within a portfolio. The time period is known as the holding period, and the probability is known as the confidence interval. Value-at-risk is not an estimate of the worst possible loss but the largest likely loss. For example, a firm might estimate its VaR over 10 days to be $100 million with a confidence interval of 95%. This would mean there is a one-in-twenty (5%) chance of a loss larger than $100 million in the next 10 days. Variation margin. During periods of great market volatility or in the case of high-risk accounts, additional margin deposited by a clearing member firm to an exchange. Vega. The amount by which the price of an option changes when the volatility changes. Vintage year. The first year an emissions allowance can be used for compliance. Volatility. A measure of the variability of a market factor, most often the price of the underlying instrument. Volatility is defined mathematically as the annualized standard deviation of the natural log of the ratio of two successive prices; the actual volatility realized over a period of time (the historic or historical volatility) can be calculated from recorded data.

450

Glossary

Volatility trading. Trading, usually through the options markets, based on the belief that implied volatility will not match the volatility actually realized over a given period or that the difference in implied volatility between different options will alter over a given period. Volume. The number of purchases and sales of futures contracts made during a specified period of time, often the total transactions for one trading day.

W Weather derivatives. Forward instruments used to hedge against or speculate on weather. Virtually all of the instruments are based on degree-days, though precipitation swaps and sunshine options are among other possible instruments. West Texas Intermediate (WTI). US crude oil used as a benchmark for pricing much of the world’s crude oil production. Wind energy. The kinetic energy of wind converted into mechanical energy by wind turbines (i.e. blades rotating from the hub) that drive generators to produce electricity.

Y Yield curve. A chart in which the yield level is plotted on the vertical axis and the term to maturity of debt instruments of similar creditworthiness is plotted on the horizontal axis. The yield curve is positive when long-term rates are higher than shortterm rates. However, yield curve is negative or inverted.

Z Zero-cost option. An option strategy under which one option is purchased by simultaneously selling another option of equal value.

I N D E X

Page numbers for figures have suffix f, those for tables have suffix t, those for equations have suffix e, and those for boxes have suffix b AA see Assigned Amount AAU see Assigned Amount Unit ACQ see annual contract quantity additionality, defined, 423 AEP see American Electric Power aggregation, defined, 423 Alaimo, Stefano, xv, 6, 355 American Electric Power (AEP), 338, 339, 343, 349 American Petroleum Institute (API), 12 American Society for Testing and Materials (ASTM) defined, 424 American style option, defined, 423 annual cap, defined, 423 annual contract quantity (ACQ) defined, 423 API see American Petroleum Institute API gravity, defined, 423 APO see average price option ARA (Amsterdam-Rotterdam-Anterp area) defined, 423 Aragones, José Ramon, xv, 67 arbitrage, 3, 9, 112, 257 defined, 423 arbitration, defined, 423 Arrow, J.K., 219, 220, 222 ASEAN see Association of Southeast Asian Nations Asia-Pacific Petroleum Conference, 290 Asian energy markets, 283–293 challenges to change, 291–293 Asian companies slow to adopt risk management tools, 293 energy risk management and hedging, 292 issues of creditworthiness and state ownership, 291 characteristics of the Asian market, 290–291 active paper market for jet fuel, 290 development of smaller markets, 290 swaps brokers in Singapore, 291 global position of Asia-Pacific region, 286–288 China seen as wild card, 288 coal commoditization possible, 288 increasing importance in world oil demand, 287 need to manage energy price risk, 287–288 major users of energy, 283–284

market development, 285–286 controlled deregulation process, 286 developments in the physical markets, 286 energy derivatives starting in the AsiaPacific region, 285–286 use of OTC derivative agreements, 286 market drivers of energy trading, 285 use of risk management techniques, 285 market evolution, 284 other fundamental changes, 288–289 increasing petroleum storage facilities, 288–289 Russian Far East oil and gas, 289 risk profile of Asian corporations, 284 security of energy supply important, 293 tanker market developments, 289–290 very large crude carriers (VLCC), 289 Asian option, 127–128 defined, 423 asphalt, defined, 424 Assigned Amount (AA), 370 Assigned Amount Unit (AAU), 369 associated-dissolved natural gas, defined, 424 Association of Southeast Asian Nations (ASEAN), 287 ASTM see American Society for Testing and Materials at-the-money option, defined, 424 atmospheric distillation, defined, 424 average freight rate assessments (AFRA), defined, 423 average price option (APO), 23

back month, defined, 424 back-testing, defined, 424 backhaul, defined, 424 backwardation, defined, 424 Baker Botts, 330 Bank of America, 393, 422 Banker’s Trust, 28 bar chart, defined, 424 Barclays Bank, 393, 422 barge lots, defined, 425 barrel, defined, 425 barrel of oil equivalent (BOE), defined, 425

451

452

barrels per calendar day, defined, 425 barrier option, defined, 425 base stock, defined, 425 base year, defined, 425 baselines, defined, 425 baseload, defined, 425 basis, defined, 425 basis risk, defined, 425 basis swap, defined, 425 basket trades, defined, 425 BCCA see Business Coalition for Clean Air Appeal Group bear market, defined, 426 bear spread, defined, 426 Bear Stearns, 3 Bellmans principle of optimality, 113 benchmark crude, defined, 426 benzene, toluene and zylene (BTZ) , defined, 427 beta, defined, 426 bid, defined, 426 bid and ask, defined, 426 bilateral energy trading, defined, 426 bilateral netting, defined, 426 bill of lading, defined, 426 biodiesel, defined, 426 biofuels, defined, 426 biogas, defined, 426 biomass, defined, 426 biosphere, defined, 426 bitumen, defined, 426 black box, defined, 426 Black-Scholes model, 128e, 239, 240f, 259 defined, 426 Blanco, Carlos, xv, 5, 67, 81, 87, 133 blender, defined, 427 blendstock, defined, 427 block trades, defined, 427 BOE see barrel of oil equivalent bottoms, defined, 427 BP, 46, 85 bpriskmanager, Global Structured Products Group, 37, 46 breakaway gap, defined, 427 breakout, defined, 427 Brent crude oil, 8, 14, 163, 215, 217, 220, 221, 286, 395 defined, 427 British Thermal Unit (BTU) defined, 427 broker-dealer, defined, 427 brokerage fee, defined, 427 brokerage house, defined, 427 broker’s deck, defined, 427 BTU see British Thermal Unit BTZ see benzene, toluene and zylene bull, defined, 427

Index

bull market, defined, 427 bull spread, defined, 427 bunker C, defined, 427 Burtraw, Dallas, 311 Bush, George W., 8 Business Coalition for Clean Air Appeal Group (BCCA), 330 butane, defined, 427 butterfly spread, defined, 427 buy and hold, defined, 427 buying hedge, defined, 428 BuySell, defined, 428

CAAA see Clean Air Act Amendments (CAAA) (1990) CAIR see Clean Air Interstate Rule calendar spread, defined, 428 California Climate Action Registry, 298, 328, 340 California Environmental Quality Act (CEQA), defined, 428 call option, defined, 428 canceling order, defined, 428 candlestick charts, defined, 428 CAO see China Aviation Oil cap, defined, 427 cap-and-trade program, 298, 311, 318, 377 defined, 428 Capital Asset Pricing Model, 228n carbon dioxide (CO2), defined, 428 carbon dioxide equivalent, defined, 429 Carbon Disclosure Project, 303, 339 carbon intensity, defined, 429 carbon monoxide, defined, 429 carbon offsets, defined, 429 carbon sequestration, defined, 429 carbon sink, defined, 429 carbon trading, 351–352, 363–390 EU Emissions Trading Scheme, 365, 375–379 cap-and-trade system, 377 drivers of carbon pricing, 377–382, 379f inter-fuel competition, 380–382, 381f new entrant reserves, 382 politics of restructuring, 378–379 supply of emission credits, 378, 379–380 key features, 376f future uncertainties, 383–384 gas-emission price relationship, 383f outlook for European gas market, 385b genesis of carbon, 364–366 global position, 384–386 Canada, 386 Japan, 384–385 why EU ETS would be price setting, 386f

Index

carbon trading (continued) impact of carbon trading on power sector, 387–389 actual reductions in emissions, 388f emissions for power (1990), 387f power generation capacity at risk, 389f Kyoto Protocol, 367–375 Assigned Amount Unit (AAU), 369, 375 Clean Development Mechanism (CDM), 369, 373–374 CDM Executive Board, 373, 374 differences beteen annex A and Annex B countries, 370 flexibility mechanisms, 368f, 369–370 Assigned Amount (AA), 370 Certified Emission Reduction (CER), 370, 372 Emission Reduction Rights (ERR), 370 European Union Allowance (EUA), 370 Removal Units (RMU), 370 Joint Implementation (JI), 369, 371f Joint Implementation Supervisory Committee (JISC), 371 key content, 367f mechanics of project-based transactions, 374–375 ratified by 119 countries, 369 multiple carbon product markets, 384 policy framework, 366–367 United Nations Framework Convention on Climate Change (UNFCCC), 364, 366 post 2012 scenario, 390, 391f trading policy involves price, security, reliability and compliance, 363–364 Carmine, Ben, 336n carrying charge, defined, 429 carryover, defined, 429 cash commodity, defined, 429 cash contract, defined, 429 cash flow at risk (CFaR), 136, 139 cash market, defined, 429 cash settlement, defined, 429 catalytic reduction systems (CSR), 332 CBOT see Chicago Board of Trade CCGT see combined cycle gas turbine (CCGT) CCRO see Committee of Chief Risk Officers CDD see cooling degree days CDM see Clean Development Mechanism Centaurus (hedge fund), 26 Center for Environmental Financial Markets, 303 CEQA see California Environmental Quality Act Certified Emission Reduction (CER), 370 defined, 429 C+F see cost and freight CFaR see cash flow at risk

453

CFTC see Commodity Futures Trading Commission chains, defined, 430 charterer, defined, 430 charting, defined, 430 charts, defined, 430 Chevron-Texaco, 85 Chicago Board of Trade (CBOT), 2 Chicago Climate Exchange, 299, 301, 328, 352 Chicago Mercantile Exchange (CME), 15, 270, 275, 282n China, 177, 283, 284, 285, 287, 288, 289 China Aviation Oil (CAO) Singapore, 81 CIF see cost insurance and freight Citigroup, 90 Clarksons (ship brokers), 290 CLD see credit loss distributions Clean Air Act Amendments (CAAA) (1990), 307, 308, 309, 310, 311 Clean Air Interstate Rule, 343 Clean Coal Power Initiative, 349 Clean Development Mechanism (CDM), 298, 340, 369 defined, 430 Clean Energy States Alliance, 351 clearing corporation, defined, 430 clearing member, defined, 430 clearinghouse, defined, 430 Clewlow, L., 79n, 116 climate change, defined, 430 climate risks and electric utilities, 337–353 see also electricity options; weather risk carbon dioxide emissions from power stations probably aid climate change, 338 carbon trading, 351–352 climate change will impact on power stations, 337–338, 352 coal gasification, 348–349 integrated coal gasification combined-cycle technology (IGCC), 348, 349 projects by power companies, 348, 349 support for development in US, 349 electric power companies, 341–348 future economics of electric power uncertain, 341–342 need for improved efficiency, 347–348 new construction will add to carbon dioxide pollution, 342–343 portfolios of mitigation measures, 345–347 range of management strategies, 345 stock performance based on environmental ratings, 344f use of combined-cycle gas turbine (CCGT) plants, 348 investors in electric utilities are concerned, 339

454

climate risks and electric utilities (continued) carbon disclosure project, 339 Investor Network on Climate Risk, 339 physical damage from hurricanes, 338 renewable power technology, 350–352 wind power growth, 350 states taking the lead in the US, 340–341 California Climate Action Registry, 340 California Public Service Commission, 340 Climate Trust in Oregon, 340 Regional Greenhouse Gas Initiative, 340 Wisconsin Public Service Commission, 340 Climate Trust in Oregon, 340 closed-end funds, defined, 430 closing price, defined, 430 CME see Chicago Mercantile Exchange coal, defined, 431 cogeneration, defined, 431 coke (petroleum), defined, 431 collar, defined, 431 ‘collar’ derivative package, 44 Colonial pipeline, defined, 431 Colorado Public Utility Commission, 346 combined cycle, defined, 431 combined cycle gas turbine (CCGT), 348 Committee of Chief Risk Officers (CCRO), 135, 143, 246, 252 Commodities Now, 184 commodity, defined, 431 Commodity Exchange Act (CEA), defined, 431 commodity future, defined, 431 Commodity Futures Trading Commission (CFTC) Commitment of Traders report, 8, 11, 100, 395 defined, 431 commodity swap, defined, 431 commodity trading advisor (CTA), 395 condensate, defined, 431 Conference of Parties (COP), defined, 431 contango, defined, 431 contingent swap, defined, 432 continuation chart, defined, 432 contract, defined, 432 contract for differences (CFD), defined 430 conventional thermal electricity generation, 432 convergence, defined, 432 cooling degree days (CDD), 273 defined, 282n Cootner, P., 168 copulas, 92, 242, 243n correlation coefficient, defined, 432 cost and freight (C+F), defined, 428 cost basis, defined, 432 cost insurance and freight (CIF), defined, 430 cost of carry, defined, 432

Index

covariance, defined, 432 cover, defined, 432 covered option, defined, 432 crack spreads, defined, 432 credit loss distributions (CLD), 88–89, 89f credit risk management, 81–93 counterparty credit risk charges, 88–89 credit loss distribution graph, 90f expected loss (EL), 89 credit loss distributions (CLD), 88–89, 89f economic capital and credit risk, 89–92 approaches to aggregation of risk, 92 copula approach, 92 joint normality of risk, 92 perfect inter-risk correlations, 92 simulation of risks, 92 diversified economic capital, 91–92 marginal economic capital, 91 return on capital vs income growth for Citigroup, 91f risk-adjusted return on capital (RAROC), 90 stand-alone economic capital, 91 internal credit risk rating system (IRRS), 82–83 Moody’s KMV one year expected default frequency (KMV EDF), 82, 83b, 84f senior credit risk committee, 83 potential future exposure, 83–88 dynamic market-based counterparty credit limits, 85 VaR enhanced counterparty risk report, 85f mark to market (MtM) value, 84 modeling, 86–88 expected exposure (EE), 86 maximum potential future exposure (MPFE), 86 potential future exposure (PFE), 86 graph of exposure for commodity swap, 87f testing and vetting models, 88 use of simulation approaches, 87 cross-hedging, defined, 432 Crouhy, M., 93, 143 crude oil, defined, 432 CSR see catalytic reduction systems CTA see commodity trading advisor cubic foot of natural gas, defined, 432 customer margin, 433

daily trading limit, defined, 433 day order, defined, 433 day traders, defined, 433 Deaton, A., 171 deep-in-the-money, defined, 433

Index

default, defined, 433 deferred (delivery) month, defined, 433 delivery, defined, 433 delivery points, defined, 433 delta, defined, 433 Delta Airlines, 82, 84 delta-hedged, defined, 433 delta neutral, defined, 433 demand-side management (DSM), defined, 433 Department of Energy (US), 12 derivative, defined, 433 derivatives structures, 31–50 see also over-thecounter energy derivatives market basic derivatives instruments, 35–42 digital options, 41 energy derivatives instrument defined, 35 fixed-for-floating swap, 36 consumer hedged with swap P&L diagram, 37f energy consumer buys a swap to remove floating price exposure, 38f P&L diagram, 37f ‘proxy swaps,’ 37 transaction diagram, 36f hedging as principal use, 36 quanto derivatives, 42 refinery ‘basket options,’ 42 swaptions, 41–42 call swaption transaction diagram, 41f vanilla options, 37–40 consumer hedging with call option, 40f producer hedging with put option, 40f transaction diagram, 39f derivative packages, 42–46 ‘collar,’ 44 P&L diagram, 44 zero-cost transaction diagram, 45f defined, 42 extendible swap, 45, 46f reference swaps, 42 net flow diagram, 43f transaction diagram with energy risk manager, 43f three-way options, 44–45 market risk exposure, 33–35 company ‘position,’ 33 consumer profit and loss (P&L) diagram, 36f consumer transaction diagram, 35f graph of producer risk exposure, 34f producer transaction diagram, 34f ‘underlying’ commodity prices, 33 risk and risk management, 31–32 commodity market price risk, 32 objectives of risk management, 32 packages of derivative instruments, 32

455

risk defined, 31 wide range of risks, 31 structuring, 46–50 defined, 46 example of bids for asset, 47–50 monetization of options, 49f transaction diagram, 49f example of Japanese LNG importer, 46–47 exchange of JCC exposure for WTI, 46, 47f reference swap transaction diagram, 48f Deutsche Bank, 400, 422 differentials, defined, 434 direct load control, defined, 434 discounting, defined, 434 distributed generation, defined, 434 DiTomasso, J., 176 divergence, defined, 434 double bottom, defined, 434 double top, defined, 434 Dow, Charles, 148 Dow Jones Daily Price Index for electricity, 262 Dowd, Kevin, xv, 5, 67, 81, 93, 133, 143 downside risk, defined, 434 downstream, defined, 434 Dresdner Kleinwort Wasserstein, 352 Drivenes, Asmund, 51 DTE Energy Ventures, 351 dual trading, defined, 434 Dubai Brent crude oil, 8, 290 Dubai Commodity Exchange, 16 Dubrouecq, Florence, 315

Eagleeye, Joseph, 185 early crediting, defined, 434 EDF see Electricité de France; expected default frequency EE see expected exposure EEX see European Energy Exchange Eischengreen, B., 403 Electricité de France (EDF), 223 electricity options, 235, 255–268 see also climate risks and electric utilities basic calls and puts, 257–258 electricity unique among traded commodities, 255–256 Greeks, 258–259 delta, rate of change of value, 258 gamma, change in delta, 258 theta, rate of change with time, 259 vega, change in value with volatility, 259 history, 256–257 NYMEX first electricity option contract (1996), 256

456

electricity options (continued) OTC option products, 262–265 Black 76 model formulae, 263b limitations of models, 265 ‘monthly’ and ‘daily’ European options, 263 ‘spark rate’ and ‘heat rate’ options, 263 spread option model valuation, 264b swaption valuation, 265b swaptions defined, 264 typical contracts, 262 valuing daily options, 264b potential portfolio applications, 265–267 demand portfolios, 265–266 option applications, 267b supply portfolios, 266 structures, 261–262 collar, defined, 262 straddle, defined, 261 strangle, defined, 262 vertical spread, defined, 262 users, 257 hedgers, speculators and arbitrageurs, 257 valuation, 259–261 Black-Scholes formula, 259 implied volatility, 260 interest rates, defined, 260 strike price, defined, 259 time to expiration, defined, 260 volatility, defined, 260 volatility skew as a problem for electricity traders, 260–261 electronic order, defined, 434 electronic trading system, defined, 434 Ellerman, Denny, 311, 314 Elliott, R.N., 158 Elspot see Nordic electricity exchange Elspot market trades contracts, 56 emission reduction credit (ERC), 327, 333, 334, 336 emission reduction unit (ERU), 370, 374, 378 defined, 435 emission scenario, defined, 435 emissions, defined, 435 Emissions Specifications for Attainment Demonstration (ESAD), 336n emissions trading, defined, 435 Energy Act (1991) (Norway), 51 energy demand, defined, 435 energy efficiency (EE), defined, 435 energy futures today, 7–16 development of futures market, 7–8 future of energy futures, 14–16 increases in trading volumes, 16 IPE change to open outcry and becomes IntercontinentalExchange (ICE) (2005), 14–15

Index

IPE trading Brent oil (1988), 14 NYMEX as epicenter of oil futures, 14 NYMEX Clearport system, 15 ‘over the counter’ markets (OTC), 15 futures and risk, 8–10 domination of futures by financial interests, 9 OPEC and NYMEX prices, 10t role of funds, 9 US Commodity Futures Trading Commission’s Commitment of Traders report, 8 new market fundamentals, 11–14 economic and geopolitical risks, 12 effects of invasion of Iraq, 13 physical risks to assets, 11 rise in US imports of crude oil, 13 widespread news coverage of oil factors, 13–14 NYMEX contracts, 8 risk premiums, 10–11 new era of market participants, 11 ‘war premium’ (2002), 10 Energy Hedge Fund Center, 400, 413 Energy Information Administration (US), 305, 342, 348 energy market developments, 413–422 creation of global markets, 413 energy hedge funds, 417–420 increase the risks of the energy business, 420 market volatility will be challenge, 417 energy will be a major bull market, 416–417 environment is an immature financial market, 421 increased speed of trading, 414 Internet increases scope of globalization, 415 investors buying the physical assets, 414 nature of risk-taking, 418–420 rating of hedge funds by Moody’s KMV, 418 size matters in energy trading, 422 survival of the fittest, 422 types of risk that need to be managed, 416 Energy Merchant, 246 Energy Policy Act (2005), 349, 350 energy risk manager, 42 energy trading, transaction and risk management (ETRM) software, 245–254 current status, 247–250 heterogenous market, 247 integration, 249–250 requirements dictated by assets and location, 248 traditional software business model, 248 vendors, 248–249

Index

energy trading, transaction and risk management (ETRM) software (continued) dichotomy of requirements, 250–251 history, 245–247 natural gas regulation (1992), 246 new era, 251–252 benefits of new technology, 251–252 risk management tools and methods, 253–254 Enerstrat Consulting, 372, 373, 374, 375, 379 enhanced recovery, defined, 435 Enron, 4, 8, 9, 81, 107, 108, 134, 247 cause of energy traders going out of business, 27–28, 394 liquidity crisis, 142–143 set the standard for energy trading, 27 Entergy/Koch, 394 environmental financial markets, 295–306 climate change as investment opportunity, 303–306 Carbon Disclosure Project, 303 cleantech investment indexes, 304 Investor Network on Climate Risk, 303 knowing the risks, 304–305 current market developments, 297–299 ‘cap-and-trade’ program, 298 carbon market, 299 Clean Development Mechanism (CDM), 298 GHG regime, 298 nitrous oxides (NO2) controls, 297 sulfur dioxide (SO2) controls, 297 environment now a corporate financial issue, 296, 305 EU Emissions Trading Scheme (ETS), 297, 298, 299, 318, 365 green trading, 298–301 carbon dioxide (CO2) as global commodity trading market, 299 ‘green finance,’ 300–301 linking climate exchanges and futures exchanges, 301 triple convergence in environmental financial markets, 300f investment model for green finance, 301–303 clean technology, 302 high energy prices justify investments, 302 hybrid of venture capital, hedge funds and private equity, 301–302 Kyoto Treaty, 297, 298, 305 problems of energy and the environment need long term solutions, 305–306 Environmental Protection Agency (EPA), 2, 308–310, 341 equity, defined, 435 ERC see emission reduction credit ERU see emission reduction unit (ERU)

457

ES see expected shortfall (ES) risk measure ESAD see Emissions Specifications for Attainment Demonstration ETRM see energy trading, transaction and risk management (ETRM) software ETS see European Union Emissions Trading Scheme EUA see European Union allowance European Climate Exchange, 301 European Commission, 223 European Energy Exchange (EEX), 217 European energy markets, 209–227 creation of risk, 210 energy markets as a dynamic system, 210f evolution of energy prices in Europe, 211f increase of $63 billion in energy imports (2002-2004), 213 intra-fuel competition, 213 need to import 50% of energy consumption, 212 structure of the European energy system, 212f financial risk transfer, 219–223 Brent crude traded on International Petroleum Exchange (IPE), 219–223 imperfectly aggregating markets, 222 importance of ideosyncratic factors, 221 Intercontinental Exchange (ICE), 222 over the counter (OTC) markets, 221–223 principal non-hydrocarbon exchanges in Europe, 221f restrictions to hedging operations, 223 impact of policy and regulation, 223–227 environmental effects, 224 EU Emissions Trading Scheme, 224 Kyoto protocol, 224 regulatory risks, 225 risk transfer, 213–219 coal price, 217 monthly prices for electricity and coal, 218f crude oil refined into products, 214 formula for relationship between European energy prices, 219e gas and oil prices decoupled, 216 prices for Brent crude and UK gas, 217f gas cost pass-through, 216 gas price formula, 215e ‘ideosyncratic factors,’ 219 National Balancing Point (NBP) gas hub, 216–217 netback pricing scheme, 214 primary energy risk transfer in Europe, 219 refining margin, 214, 215f

458

European Power Daily, 222 European Spot Gas Markets, 222 European Union, 209, 360 European Union Allowance (EUA), 63, 64, 65, 360, 361, 370 European Union Emissions Trading Scheme (ETS), 224, 297, 298, 318, 340, 355–356, 360 Eurostat, 212 exchange for physicals, defined, 435 exchange traded fund (ETF), defined, 435 exercise, defined, 435 exercise price, defined, 435 exit, defined, 435 expected default frequency (EDF), 82 expected exposure (EE), 86 expected shortfall (ES) risk measure, 68 expiration, defined, 435 expiration date, defined, 435 extendible swap, 45, 46f

Federal Energy Regulatory Commission (FERC), 105, 246, 436 Federal Reserve Board, 180, 181 interest rates, 13 feedstock, defined, 436 fibonacci retracement levels, 157, 158f, 159 Financial Accounting Standards Board (FAS), 234, 436 float, defined, 436 floor broker, defined, 436 forex market, defined, 36 Former Soviet Union (FSU), 371 forward (cash) contract, defined, 436 forward curve, defined, 436 forward rate agreement (FRA), defined, 436 fossil fuel, defined, 436 FPL Energy, 350 free on board (FOB), defined, 436 frequency distribution, defined, 436 front loaded, defined, 436 front month, defined, 436 front-running, defined, 436 FSU see Former Soviet Union fundamental analysis, defined, 437 fundamentals, defined, 437 Fusaro, Peter C., xv, 1, 6, 172, 185, 283, 295, 393, 412, 413 future volatility, defined, 437 futures commission merchant (FCM), defined, 437 futures contract, defined, 437 futures exchange, defined, 437 futures market, defined, 437

Index

Galai, D., 93 gamma, defined, 437 Gann, W.D., 158 gap, defined, 437 gas oil, defined, 437 gas to liquids, defined, 437 GHG see greenhouse gas Global Change Associates, 1 global warming, defined, 437 GME see Italian Power Exchange (GME) Goldman Sachs, 3, 303, 393, 414, 422 Greeks, defined, 437 green trading see environmental financial markets greenhouse gas (GHG), 295, 298, 305, 355 defined, 437 Greenhouse Gas Markets, 328 Greenspan, Eric, 181 grid, defined, 437

Hayden, Frank, xv, 5, 95 HDD see heating degree day head and shoulders, defined, 438 heat content of fuel, defined, 438 heating degree day (HDD), 275 defined, 282n heating oil, 2, 18, 234 heavy crude oil, defined, 438 hedge funds, 26, 393–412 activities largely hidden, 395 Centaurus, 26 continued increases in hedging and speculation, 398–399 defined, 402–403, 438 energy trading rebuilt, 407–410 energy is the most complex commodity for trading, 408 hedge funds have great flexibility, 408 new traders may be ignorant of the risks, 410 wide variety of positions being taken, 408–409 future, 410–412 energy market still small compared with equity market, 411 global oil demands may be slowing, 411 hedge funds will increase in sophistication and size, 412 oil price high and volatile, 410–411 MotherRock, 26 oil and gas prices, 405–407 factors determining price of West Texas Intermediate (WTI) crude, 405–406 gas prices now tend to follow oil, 407 oil market place volatility, 397

Index

hedge funds (continued) oil trading opportunities, 399 speculation and volatility, 405 structural changes in commodity trading, 399–402 mean price reversion, 400–402 oil companies forecast lower prices, 399–402 OPEC less able to control prices, 400 possible new era in oil price, 402 types of funds, 403–404 emerging markets, 404 equity hedge, 404 event-driven strategies, 404 macro, 403 relative value strategies, 403–404 short selling, 404 use of commodity indexes, 395–396 hedger, defined, 438 hedging, defined, 438 Henderson, J., 343 Henry Hub natural gas futures, 1, 8, 33, 99 Heren Energy, 222 high, defined, 438 historic volatility, defined, 438 Horizon Energy, 414 Houldsworth, Mark, xv, 5, 111 Howell, R., 177 Humphreys, B, 173 Hussein, Sadam, 13 hydrocarbon, defined, 438

ICE see Intercontintal Exchange ICF Consulting, 331 IGCC see integrated coal gasification combinedcycle technology implied volatility, defined, 438 independent introducing broker, defined, 438 Innovest Strategic Investors, 338, 344, 345, 353n integrated coal gasification combined-cycle technology (IGCC), 348, 349 intercommodity spread, defined, 439 Intercontintal Exchange (ICE), 15, 18, 22, 24, 25, 107, 222, 297 Intergovernmental Panel on Climate Change (IPCC), 438 internal credit risk rating system (IRRS), 82–83 International Energy Agency, 9, 350, 399, 400, 412n International Petroleum Exchange (IPE) (London), 14, 18, 51, 220, 290, 439 International Swaps and Derivatives Association (ISDA), 187–207, 234 Appendix A, 201–206 attachment, 203–206 ISDA agreement, 201–203

459

Appendix B sample letter, 206–207 confirmations, 190–191 differences between ISDA (2002) and ISDA (1992), 195–201 key changes, 196–201 bankruptcy and termination, 197–198 breach of agreement, 196 credit default, 196 credit on merger, 198–199 cross-default, 197 deferral of payments, 199 early termination, 199 failure to pay, 196 fore majeur, 198 payments on termination, 200 right to terminate, 199–200 set-off, 200–201 transaction default, 196 summary, 195 document processing, 191–194 counterparty risk, 193–194 events of default, 193–194 documentation and payment risk, 194 legal risks, 192–193 currency and law, 193 events in key area, 192–193 inconsistency, 192 single agreement, 192 transfer, 193 market risk, 194 memoranda and articles of association, 191 ISDA defined, 187, 439 Master Agreement (1992), 187, 189 (2002), 188 Master Swaps Agreement Multi-currency (1992), 188 Operational Benchmarking Survey (2003), 188 publications, 189–190 Commodity Derivatives Definitions (1993), 189 Definitions and Annex, 190 Supplement (2000), 189–190 Schedule, 187 trading before signature, 194–195 role of Schedule, 194 intrinsic value, defined, 439 introduction, 1–6 energy as an immature financial market, 3 relationship between physical and financial energy markets, 3 market drivers of energy trading, 3–4 Asian corporations entering energy trading, 4 electric power marketing, 4

460

introduction (continued) need for energy industry trade participation, 1 organization of this book, 5–6 types of risk management, 1–2 Investor Network on Climate Risk, 303, 339 IPE see International Petroleum Exchange (IPE) (London) IPE trading Brent oil (1988), 14 Iraq, 13 IRRS see internal credit risk rating system ISDA see International Swaps and Derivatives Association Isherwood, Guy, 185 Italian Power Exchange (GME), 357, 359t

James, Tom, xv, 5, 6, 17, 145, 187, 283 Japanese Custom-Cleared crude oil prices (JCC), 33 JCC see Japanese Custom-Cleared crude oil prices jet fuel, defined, 439 joint implementation (JH), 369, 371f defined, 439 Journal of Financial Economics, 240 Journal of Futures Marketing, 167

Keynes, John Maynard, 167 Kremke, Kevin, xvi, 133 Kristufek, Bob, xvi, 6, 255 Kyoto Protocol, 63, 64, 224, 328, 355, 360, 363, 364, 365f, 369 defined, 439 Kyoto Treaty, 295, 297, 305

Lack, Randall, xvi, 6, 327 LaR see liquidity at risk Laroque, G., 171 Larry, Carl, xvi, 5, 7 Larsen, Per Otto, 51 last trading day, defined, 439 least squares Monte Carlo (LSMC), 117–118 leg, defined, 439 leg out, defined, 439 Leppard, Steve, xvi, 6, 31, 32, 42, 46, 50 Lettau, M., 172 leverage, defined, 439 LGD see loss given default lifting, defined, 439 lignite, defined, 439 limit order, defined, 439 limit up, limit down, defined, 440 limits, defined, 440 linkage, defined, 440 liquidate, defined, 440

Index

liquidity, defined, 440 liquidity at risk (LaR), 136 liquidity risk measurement and management, 133–143 example of Enron collapse, 142–143 framework, 133, 134f infrastructure, 140–143 information systems, 140 S&P guidelines for liquidity adequacy, 141b types of collateral and margin payments, 142t liquidity risk modeling, 136–140 cash flow at risk (CFaR), 136 comparison of LaR and VaR, 137b liquidity at risk (LaR), 136 mark-to-market earnings (MtM), 138 monitoring and validating, 139–140 stress tests, 138–140 requirements for each scenario, 139 scenario analysis, 138f value at risk (VaR), 137 policies and procedures, 134–135 delegation of authority, 135 forward-looking liquidity adequacy disclosure, 136f identifying extent of liquidity risk, 134 reliability of credit, 135 types of liquidity risk, 136b liquified natural gas (LNG), 33, 46, 47, 283, 284, 384, 391 defined, 440 Litteman, R., 170 LNG see liquified natural gas load management, defined, 440 local, defined, 440 London Clearing House, 15 London Stock Exchange, 304 long, defined, 440 long hedge, defined, 440 loss given default (LGD), 81 low, defined, 440 LSMC see least squares Monte Carlo Ludvigson, S., 172 Lund, Per Christer, xvi, 51

Malaysian Tapis Oman crude oil, 290 margin call, defined, 440 Mark, Robert, 67, 81, 87, 133, 143 mark to market (MtM) value, 84, 138 marked to market, defined, 440 market-based pricing, defined, 440 market maker, defined, 440 market risk, defined, 441

Index

market risk measurement and management, 67–78 estimating risk measures, 69–73 advantages of Monte Carlo, 71 mean-reverting jump-diffusion process, 71–73 estimated risk measures, 73t Omstein-Uhlenbeck equations, 71e, 72e simulated price-path, 73f Monte Carlo simulation methods, 70 non-parametric methods, 69–70 parametric methods, 70 modeling spreads, 74–77 assumptions often unrealistic, 74–75 correlations not an adequate measure of dependence, 77 correlations unstable, dependent on time, maturity and nature of price shock, 76–77, 76f factors that influence physical markets, 74 mean reversion of spreads, 74, 75f two-factor models, 74–77 organization of risk management, 78 stress tests and scenario analysis, 77–78 needed for senior management, 77 ‘stress test committee,’ 77–78 types of risk measure, 68–69 coherent risk measures, 68 expected shortfall (ES), 68 is VaR a good risk measure, 69b value at risk (VaR), 68 market sentiment, defined, 441 market timing, defined, 441 mass emissions cap and trade (MECT), 330, 331, 332 Massachusetts Institute of Technology (MIT), 311, 353n Mathieson, D., 403 Mauro, Alessandro, xvi, 5, 209, 220, 225, 227, 228 maximum potential future exposure (MPFE), 86 McGraw-Hill, 19 MDA Federal/EarthSatellite Corporation, 270 mean, defined, 441 Mean of Platts Singapore (MOPS), defined, 441 mean reverting, defined, 441 Measurisk (software), 181 MECT see mass emissions cap and trade Merrill Lynch, 394, 422 Miller, Nedia, xvi, 5, 231 momentum, defined, 441 Monte Carlo simulation methods, 67, 70, 71, 85, 117–118, 126, 127, 240, 253, 259 Montreal Protocol, defined, 441 Moody’s KMV, 82, 418 Morgan Stanley, 3, 351, 393, 401, 414, 422 MotherRock (hedge fund), 26

461

moving average, defined, 441 MPFE see maximum potential future exposure MtM see mark to market Murdoch, Warren, 81

NAP see National Allocation Plan naphtha, defined, 441 naphthenic naphtha, defined, 441 National Allocation Plan (NAP), 360 National Environmental Policy Act (NEPA), defined, 441 National Futures Association (NFA), defined, 441 natural gas liquids (NGL), defined, 441 natural gas options, 235 natural gas storage, defined, 441 natural gas trading, 95–109 see also structured transactions in natural gas characteristics of natural gas risk, 104–106 basis, 104–105 correlation, 105 index, 105–106 returns on risk capital, 106 price, 104 consumption commodity, 96 physical and financial markets coexist, 96 financial market, 98–100 NYMEX futures is an opinion about the price of natural gas at Henry Hub, 99 success of NYMEX, 100 supply-demand factors, 99–100 ability to transact, 99–100 access to the marketplace, 99 branding and confidence, 100 future, 108–109 new participants enter the market, 108 only large hedge funds will remain, 109 physical market meets the financial, 101–104 future forward, 102, 103f supply-demand curves for physical market at expiry, 101, 102f weather, 103–104f pipeline grid, 96–97 importance of storage, 97 properties of natural gas, 95–96 supply and demand curves, 97, 98f US gas industry, 106–107 acceptance of NYMEX contracts, 107 wellhead prices deregulated, 107 NBP see UK National Balancing Point negawatt, defined, 442 New York Harbor unleaded gasoline, 8 New York Mercantile Exchange (NYMEX), 234 Clearport System, 15, 26 crude oil contract (1983), 11, 297

462

New York Mercantile Exchange (NYMEX) (continued) defined, 442 epicenter of oil futures, 14 Henry Hub natural gas futures, 1, 8 initial trade of No.2 heating oil (1978), 2, 18 recent growth, 8 nitrogen oxides, defined, 442 nitrous oxides (NO2) controls, 297 non-attainment area, defined, 442 Nord Electricity see Nord Pool Nord Pool electricity futures exchange, 51 Spot power exchange, 56–59 Nordic electricity exchange (Elspot), 54 Nordic electricity markets, 51–67 comparison of power generation, 53f electricity derivatives market, 60–63 administrative risk, 63 certificates for difference (CfD) for each area, 61–62 counterparty risk, 62–63 graph of market development, 61f liquidity and currency risks, 62 need for risk management, 61 Nord Pool system price, 62f variety of instruments available, 60 electricity exchange (Elspot), 54 emissions market, 63–65 effect of trading EUAs on power prices, 64 EUA prices, 64f Kyoto protocol, 63, 64 Nord Pool is leading marketplace, 63 history and development, 55f, 56 Nordic Power Exchange, 55 map of the market, 52f market characteristics, 54, 57–60 Elbas market offers continuous trading, 58f predefined bid areas, 57 settlement, 59–60 Nord Pool Spot AS acts in all contracts, 59–60 participants can be physical or financial, 59 Nord Pool Spot, the physical day-ahead market, 56–59 determination of day-ahead market clearing prices, 56 aggregate supply/demand curves for pricing, 57f Elspot market trades contracts, 56–57 congestion management, 57 ownership and stakeholder relations, 54–55 pie chart of total fuel mix, 53f transmission system operators (TSO), 54

Index

normalized, defined, 442 notice day, defined, 442 NYMEX see New York Mercantile Exchange

Oakes, Jason, xvii, 6, 255 offer, defined, 442 offset, defined, 442 O’Hearne, Brian, 269 OPEC see Organization of Petroleum Exporting Countries open, defined, 442 open interest, defined, 442 open outcry, defined, 442 opportunity costs, defined, 442 option buyer, defined, 442 option contract, defined, 442 option premium, defined, 442 option seller, defined, 442 option spread, defined, 443 options, 231–243 backwardation versus contango, 232, 233f basic derivatives structures, 233–237 Asian options, 237 barrier options, 237, 238f basis risk relates to price risk, 233–234 energy options and OTC derivatives created for risk management, 233 hedging assumes negative price changes, 234 listed options on NYMEX, 235 OTC traded options provide caps, collars and floors, 235f types of swap contracts, 237f commodities are assets with non-standard structure, 232 defined, 442 derivatives and hedging for commodities, 231–232 option pricing methodology, 238–239 definition of intrinsic value, time value, fair value and value date, 238 premium and strike price, 238 volatility, 238–239 example of Brent crude calls, 239f models, 239–242 Black-Scholes, 239, 240f Monte Carlo, 240 options on spreads, 240–243 calendar, crack and spark spreads, 241 problem of models, 242 use of copulas, 242 order, defined, 443

Index

Organization of Petroleum Exporting Countries (OPEC), 9, 399, 400 out-of-the-money, defined, 443 over-the-counter energy derivatives market (OTC), 15, 17–30 see also derivatives structures chronology of energy trading, 28–30 conclusions, 30 importance of capital flow, 30 OTC, NYMEX and ICE have blended, 30 consequences of recent changes, 28 high prices and fast movements, 28 record volumes and volatility, 28 convergence of OTC and futures, 24f defined, 443 futures contracts settlement on expiry, 24–25 only 2% physical and cash delivery, 24–25 OTC instruments, 22–23 advantages of APOs, 23 average price options (APO), 23 differences between APOs and NYMEX traded options, 23 factors to take into account, 22 swaps, defined, 22, 25 OTC trading, 20–22 basic futures trade transaction flow, 21f ‘initial margins’ and ‘variation margins,’ 21–22 overview of energy markets, 18–20 development of NYMEX, IPE and ICE, 18 liquidity of energy OTC markets, 19 main OTC trading/pricing hubs, 20f role of OTC derivatives, 18 settlement of swaps contracts on expiry, 25–28 80% of OTC trades standardized, 27 factors in negotiations, 25 NYMEX Clearport system, 26 safety nets, 26

PD see probability of default peak load, defined, 443 PFE see potential future exposure Pisano, Leonardo, 158 pit, defined, 443 Platts (oil price publication), 19, 20, 26, 222, 229n Point Carbon, 297 position, defined, 443 position limit, defined, 443 position trader, defined, 443 potential future exposure (PFE), 86 Premia Capital Management, 185 premium, defined, 443 price limit, defined, 443 PRMIA see Professional Risk Managers’ International Association

463

probability of default (PD), 81 production sharing agreement, defined, 443 Professional Risk Managers’ International Association (PRMIA), 2, 185, 416 program trading, defined, 443 prototype carbon fund, defined, 443 purchasing hedge, defined, 444 put option, defined, 444

quantile, defined, 79n

RACT see Reasonably Available Control Technology range, defined, 444 RAROC see risk-adjusted return on capital ratio, defined, 444 realized/unrealized P/L, defined, 444 Reasonably Available Control Technology (RACT), 334–335 redidual fuel oil, defined, 445 reference price, defined, 444 reference swaps, 42 refiner acquisition cost of crude oil, defined, 444 refinery output (petroleum), defined, 444 refinery processing gain (petroleum), defined, 444 reformulated gasoline, defined, 444 regional emission markets, 327–336 Houston/Galveston Mass Emissions Cap and Trade (MECT) program, 330–333 graph of nitrous oxides allowance stream allocation, 331f main emitters are refineries and chemical works, 332 major nitrous oxide (NO2) reduction required, 330 operational problems, 333 power plants can use catalytic reduction systems (CSR) to minimise nitrous oxides, 332 value of trades, 331 rapidly growing business in 35 regions of US, 327 San Joachim Valley emission reduction credit trading program (ERC), 333–336, 334 different from Houston/Galveston MECT program, 335 effect of California energy crisis, 334 emissions of reactive organic gases, 335f problems due to extent of pollution, 334 quantities of emissions traded, 333 use of Reasonably Available Control Technology (RACT), 334–336 US emissions trading regions, 328t–330t

464

Regional Greenhouse Gas Initiative (RGGI), 318, 328, 340 regression, defined, 444 relative strength index (RSI), 160–161 defined, 444 Removal Units (RMU), 370 renewable energy credits (REC), defined, 444 renewable energy resources, defined, 444 renewable portfolio standards (RPS), defined, 445 renewable power technology, 350–352 Repetto, R., 343 reportable positions, defined, 445 reserve requirements, defined, 445 resistance, defined, 445 Resources for the Future, 311 retracement, defined, 445 reversal gap, defined, 445 RGGI see Regional Greenhouse Gas Initiative risk-adjusted return on capital (RAROC), 90 risk (implied), defined, 445 risk management, defined, 445 risk management in energy-focused commodity futures, 167–185 commodity price behaviour, 170–171 volatility, skewness and kurtosis, 171 deep out-of-the-money options, 173 diversification and concentration risk, 173–174 annualized portfolio volatility, 174f exit strategy, 173 extraordinary stress testing, 180–181 macro-hedging using interest rates, 180 need to ensure that futures investment is not correlated with the equity market, 180 risk management software, 181 fundamental drivers of a strategy, 174–180 example of corn and natural gas, 174–177 corn prices versus natural gas prices daily prices (2005), 177f high correlation (1999), 175f low correlation (1998), 175f normally unrelated, but related by ‘weather fear premium,’ 174–176 example of crude oil, soybeans and copper, 177–180 April-May copper and soybean prices, 179f April-May crude and copper futures prices, 179f January-March crude and copper futures prices, 178f January-March crude and soybeans futures prices, 178f possibly related by Chinese demand, 177–180 important elements in investing, 168

Index

product design, 168–169 risk is the flipside of return, 167–168 risk management reports, 182–183 first example report, 183t measures of risk compared to limits, 182 second example report, 184t scenario testing, 172 markets are learning systems, 172 standard risk management methodology, 169–170 value at risk, 171–172 viability of a futures program, 169 risk measurement, defined, 445 RMU see Removal Units roll, defined, 445 roll-over risk, defined, 445 round turn, defined, 445 RSI see relative strength index Rubin, E.S., 347, 348, 349 Russia, 372 Russian Far East oil and gas, 289

Sarbanes-Oxley Act, 234, 246, 252, 345 Saudi Arabia, 400 seasonal trend, defined, 445 seasonality, defined, 445 Securities Exchange Commission (SEC), 402, 403 securitization, defined, 445 selling short, defined, 446 sequestration, defined, 445 settlement, defined, 446 settlement price, defined, 446 settlement risk, defined, 446 Sgarioto, R., 220 SGX see Singapore Exchange Shanghai Futures Exchange, 51 Shastri, Ashutosh, xvi, 6, 363 Shinko, D., 173 short, defined, 446 simple moving average, defined, 446 simple options see vanilla options Singapore, 287, 288, 291 Singapore Exchange (SGX), 51 SIP see State Implementation Plan (SIP) sleeving, defined, 446 slippage, defined, 446 SO2 allowance trading, defined, 446 Soronow, D., 79n sour/sweet crude, defined, 446 Southern California Edison, 81 spark spread, defined, 446 speculator, defined, 446 spike, defined, 446 spot, defined, 446

Index

spot prices, defined, 446 spread, defined, 446 spreading, defined, 446 standard deviation, defined, 446 State Implementation Plan (SIP), 330, 334 stock index futures, defined, 447 stocks, defined, 447 stop-limit order, defined, 447 stop loss, defined, 447 stop order, defined, 447 stops, defined, 447 straddle, defined, 447 strategic storage, defined, 447 Stratus Consulting, 343 Strickland, C., 79n, 116 strike price, defined, 447 strips, defined, 447 structured transactions in natural gas, 111–130 see also natural gas trading Asian options, 127–128 Black-Scholes model, 128e valuation, 127–128 intrinsic value, 113–115 backward evolution of values, 115 intrinsic value defined, 113 inventory state map, 114f, 115f value of storage (VS), 114e natural gas storage, 112–115 Bellmans principle of optimality, 113 constraints, 113t valuation techniques, 112–113 spread option bundles, 118–124 calendar spread options, 118–120 adjustments to the input, 120 Margrabe-Kirk approximation, 119e nuances to the spread option solutions, 123–124 optimization overview, 121–124 spread option values, 122t, 123t volatility term structure, 120–121 stochastic dynamic programming, 116–118 least squares Monte Carlo (LSMC), 117–118 trinomial forest, 116–117 model of forward curve dynamics, 116e structured transactions defined, 111 swaptions, 129–130 equation for payoff at termination, 129e swing options, 124–127 Bellman-like equation, 126e swing option contract terms, 125t trinomial forest or Monte Carlo implementation, 126 valuation and hedging nuances, 126–127 Stuebi, Richard T., xvi, 6, 307

465

sulfur dioxide (SO2), 297 defined, 447 swap, defined, 447 swaptions, defined, 41, 447 sweet crude, defined, 447 swing, defined, 447 swing producer, defined, 447 Swiss Re, 274t, 282n synthetic securities, defined, 447

Tabossi, Carla, xvii, 6, 337 take-or-pay, defined, 448 technical analysis, 145–165 candlestick charts, 152, 153f DOJI formation, 154f definition of technical analysis, 146–147, 448 futures bar chart, 146f trends in crowd behaviour, 147 end of trend signal, 155–157, 164 examples of double top and double bottom, 164f examples of price gaps, 156f, 157f fibonacci retracement levels, 157, 158f, 159 chart reading, 159 mathematical indicators, 159–165 moving average, 161–162 example of IPE Brent, 163f interpretation, 162–163 relative strength index (RSI), 160–161 application, 160–161 equation, 160e example of NYMEX WTI, 162f, 163t principles, 147–148 basis is Dow theory, 148 emotions play a part, 148 fundamentals are included in prices, 147 markets are repetitive or cyclical, 148 prices move in trends which persist, 147 trendlines, 148–152 bar chart of prices, 149f bearish trend, 151f breakout, 150 bull trend, 151f support and resistance, 150, 152f trendline chart, 149f VIP relationship (volume, interest and price), 153–155 chart, 155f market predictions, 154–155 technical rally, defined, 448 Texas Commission on Environmental Quality, 330 theta, defined, 448 tick, defined, 448 Till, Hilary, xvii, 5, 167, 172, 173, 176

466

time series, defined, 448 time value, defined, 448 Tjomsland, Bjorn, 51 Tocom see Tokyo Commodity Exchange Tokyo Commodity Exchange (Tocom), 51 trading bands, defined, 448 trading green, white and red certificates, 355–362 European Emissions Trading Scheme (ETS), 355–356 green trading, 356–357 encouraging electricity production from renewable sources, 356 Italian Power Exchange (GME), 357 market mechanism based on a quota and tradeable certificates (TRECs), 356 renewable obligations certificates (ROCs), 356–357 trans-mediterranean renewable energy cooperation (TREC), 356 red trading, 360–362 emission unit allowances (EUA), 360 National Allocation Plan (NAP), 360 price of EUAs, 361, 362f trading EUAs through OTC brokers, 360–361 white trading, 358–359 certificates for saving energy, 358 Italian targets organised by GME, 359t trading limit, defined, 448 trading range, defined, 448 trading sulfur dioxide (SO2) allowances, 307–324 experience to date, 311–317 allowance trades between parties, 314f annual allowance flows, 312t annual sulfur dioxide (SO2) emissions, 311f CAAA program successful, 317 cap-and-trade emissions trading regime, 311 economic gains from trading, 317f overcompliance and ‘banking,’ 312–313 reasons for increase in allowance prices, 315 spot prices, 315f lessons learned, 317–324 advantages for operators to participate in early negotiations, 321 allowance allocation is important, 321 early opt-ins can be beneficial, 322–323 financial derivatives and speculators will increase activity, 322 initial use of gas fuels may revert to coal, 323 markets wil become more efficient with time, 322 need for good accounting practices, 324 operators learn by experience, 320–321 regulators of electricity prices need to understand emissions trading, 323

Index

satisfactory trading activity in sulfur dioxide (SO2), 318–319 sulfur dioxide (SO2) experience can be applied to carbon dioxide (CO2), 320 technological benefits, 319, 320f working cap-and-trade structure for carbon dioxide (CO2) allowances in ETS, 318 sulfur dioxide (SO2) allowance trading, 308–310 amount of allowances, 309–310 annual emission requirements, 309f Clean Air Act Amendments (CAAA), 307, 308, 309, 310, 311 Environmental Protection Agency (EPA), 308–310 impetus for allowance trading, 310 options for allowance owners, 310 preventing acid rain, 308 trans-mediterranean renewable energy cooperation (TREC), 356 transmission system operators (TSO), 54 TREC see trans-mediterranean renewable energy cooperation trend, defined, 448 trend-following, defined, 448 trending market, defined, 448 trendless, defined, 448 trendline, defined, 448 trigger condition, defined, 449 TSO see transmission system operators Turnbull, S.M., 128

UK National Balancing Point (NBP) gas price, 33, 216 uncovered option, defined, 449 underlying futures contract, defined, 449 underlying instrument, defined, 449 United Nations Framework Convention on Climate Change (UNFCCC), 364, 373 defined, 449 United Nations Secretariat on Climate Change, 355 unleaded gasoline options, 235 upside risk, defined, 449 upstream, defined, 449 Utilipoint, 247, 253

value at risk measure (VaR), 68, 70, 137 defined, 449 vanilla options, 37–40 VaR see value at risk measure variation margin, defined, 449 Vasey, Gary M., xvii, 5, 245, 246, 251, 254, 393 vega, defined, 449

Index

very large crude carriers (VLCC), 289 vintage tear, defined, 449 volatility, defined, 449 volatility trading, defined, 450 volume, defined, 450

Wakeman, L.M., 128 weather risk, 269–282 see also climate risks and electric utilities effect on agricultural markets, 272 energy companies exposure, 271–272 importance of weather, 269–270 recent increase in volume of contracts, 270 users include gas and electricity utilities, 270–271 weather derivatives defined, 270, 450 weather risk solutions, 272–276 market potential, 276 volume and commodity price related solutions, 273–276 examples of weather structures by Swiss Re, 274t heating degree days (HDD), 275 weather measures available, 275–276 volume related, 272–273 cooling degree days (CDD), 273 volume example, 273t weather strategy, 276–282 calculate exposure, 277–281 evaluate cost/benefit and counterpart credit, 281 example is NatGas Inc with a HDD floor put, 278 known’s and risks are defined, 280

467

possible strategies, 279f select the hedge and define the risk, 280 compile and analyse data, 276–277 temperature of a proxy for power demand, 277f define risk/opportunity, 276 trading strategy, 281–282 Weather Risk Management Association, 282n websites cme.com, 282 commodities-now.com, 184 energyhedgefunds.com, 400 premiacap.com, 185 prmia.org, 185 swissre.com, 282 wallstreetmodels.com, 240 wrma.org, 282 West Texas Intermediate crude oil (WTI), 8, 234, 236, 405, 406, 407 defined, 450 Wilder, J. Welles, 160 Williams Co., 82, 84 wind energy, defined, 450 Wisconsin Public Service Commission, 340 World Energy Council, 347, 350 World Trade Center, 28 WorldCom, 292 WTI see West Texas Intermediate crude oil

yield curve, defined, 450

zero-cost option, defined, 450

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