Price Regulation and Risk: The Impact of Regulation System Shifts on Risk Components (Lecture Notes in Economics and Mathematical Systems)

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Lecture Notes in Economics and Mathematical Systems

641

Founding Editors: M. Beckmann H.P. Künzi Managing Editors: Prof. Dr. G. Fandel Fachbereich Wirtschaftswissenschaften Fernuniversität Hagen Feithstr. 140/AVZ II, 58084 Hagen, Germany Prof. Dr. W. Trockel Institut für Mathematische Wirtschaftsforschung (IMW) Universität Bielefeld Universitätsstr. 25, 33615 Bielefeld, Germany Editorial Board: H. Dawid, D. Dimitrov, A. Gerber, C-J. Haake, C. Hofmann, T. Pfeiffer, R. Slowiński, W.H.M. Zijm

For further volumes: http://www.springer.com/series/300

Michael Hierzenberger

Price Regulation and Risk The Impact of Regulation System Shifts on Risk Components

Dr. Michael Hierzenberger Pestalozzistraße 6/13 8010 Graz Austria [email protected] http://www.xing.com/profile/Michael_Hierzenberger

The publication of this book was financially supported by the Karl-Franzens-University Graz and the government of the province of Styria, Austria

ISBN 978-3-642-12046-6 e-ISBN 978-3-642-12047-3 DOI 10.1007/978-3-642-12047-3 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2010926590 # Springer-Verlag Berlin Heidelberg 2010 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Cover design: WMXDesign GmbH, Heidelberg, Germany printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

... Cause we are the ones that want to play Always want to go But you never want to stay And we are the ones that want to choose Always want to play But you never want to lose ... (from “Aerials” by SOAD)

Dedicated to My Family.

Preface

The present book was written within the scope of my doctoral studies in economics at the University of Graz.1 It has been a special honor of mine to be able to write my dissertation as a visiting doctoral student at the Institute for Corporate Accounting and Auditing at the University of Graz with Professor Dr. Gerwald Mandl, Head of the institute. I am very grateful for this opportunity. Additionally, I am grateful to Professor Dr. Edwin O. Fischer, Head of the Institute for Corporate Finance at the University of Graz, for his willingness to be a committee member. I am also thankful to Professor Dr. Ulrike Leopold-Wildburger from the Institute for Statistics and Operational Research for her encouragement to publish my dissertation and for her willingness to be the third committee member. Along with the goal of writing a dissertation that meets academic standards, it was also my goal to write a dissertation that serves a practical purpose. I am especially thankful to the managing directors of Energie Graz GmbH & Co KG, Mr. Dr. Gert Heigl and Mr. Dr. Rudolf Steiner, as well as to the managing directors of Stromnetz Graz GmbH & Co KG, Mr. DI Gerhard Krampl and Mr. DI Erich Slivniker. I am also thankful to Mr. Mag. Michael Mock, managing director of the Austrian Association of Gas- and District Heating Supply Companies, for his encouragement. However, my deepest gratitude belongs to my parents, Anna and Friedrich Hierzenberger, to whom this dissertation especially is dedicated. Without the insight learned from them, “nothing worthwhile comes easily”, this would not have been brought to completion. Moreover, I am grateful to my relatives, friends and colleagues for their forbearing charity during the time spent on this dissertation. Hopefully, the empirical evidence of this work will be considered in the future price regulation in Austria. Michael Hierzenberger Graz, 2009-12-08

1

This published version of my dissertation is a shorted one of the original and approbated version ¨ sterreich” (German). “Die Bestimmung von Eigenkapitalkosten regulierter Unternehmen in O

vii

Contents

1

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Purpose of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.2 Structure of the Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2

Capital Market-Based Calculation of the Cost of Equity . . . . . . . . . . . . . . . 5 2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Capital Market-Based Calculation of the Cost of Equity . . . . . . . . . . . . . . 5 2.2.1 Capital Asset Pricing Model (CAPM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.2.2 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Rate of Return on Equity as a Regulatory Parameter . . . . . . . . . . . . . . . . . 21 2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.2 Goals in Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.3 Defining Fair Prices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.3.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3

Methods of Price Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Rate of Return Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 RPI-X Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 The Principal Agent Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Rate of Return Regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.2 Diversification with Price-Based Regulation . . . . . . . . . . . . . . . . . . . 3.2.3 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Regulatory Systems and Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 Buffering Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Regulatory Lag Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.3 The Risk Effect from a Regulatory System Shift . . . . . . . . . . . . . . 3.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

29 29 29 30 30 31 37 38 39 39 40 41 44

ix

x

Contents

4

Empirical Secondary Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Stigler and Peltzman’s Theory of Regulation . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Methodology: Event Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.1 Estimation Period, Event Window and Postevent Window . . . . 4.3.2 Measuring Abnormal Returns . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.3 Hypotheses’ Derivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.5 Empirical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

45 45 46 47 48 49 53 57 60 62

5

The Primary Empirical Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Hypotheses and Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Database . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Empirical Analysis: Structural Break Analysis . . . . . . . . . . . . . . . . . . . . . . . 5.2.1 Systematic Risk and Welfare Effect: Verbund . . . . . . . . . . . . . . . . . 5.2.2 Systematic Risk and Welfare Effect: EVN . . . . . . . . . . . . . . . . . . . . . 5.2.3 Systematic Risk and Welfare Effect: DJ600UTIL . . . . . . . . . . . . . 5.2.4 Unsystematic Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.5 Total Risk and Return Averages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.6 Summary of the Structural Break Analysis . . . . . . . . . . . . . . . . . . . . 5.3 Empirical Investigation: Event Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 The Event List . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 Event Study: Constant Beta Factor; 3-Day Event Window . . . 5.3.3 Event Study: Dummy Variables from 1 July 2005; 3-Day Event Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.4 Event Study: Constant Beta Factor; 1-Day Event Window . . . 5.3.5 Event Study: Dummy Variables from 1 July 2005; 1-Day Event Window . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.6 Summary of the Event Study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

67 67 67 69 81 84 85 86 88 91 91 94 97 101

6

105 112 118 123

Summary of the Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

Chapter 1

Introduction

The market-based fundamental principle of competition is not present for natural monopolies. For this reason, monitoring corporations that possess a natural monopoly is necessary within the scope of price setting, in order to minimize welfare loss due to the lack of effect from competition. Network infrastructures, such as electricity and gas networks, are classic examples of natural monopolies. This monitoring function for the process of price setting is administered by the state or through an institution authorized by the state (a regulatory agency) for this purpose. The interests of the state and of the monopolist are diametrically opposed to one another according to the assumption of a rationally acting subject. The monopolist attempts to maximize their profits by setting prices accordingly. The goal of the state is production and consumption of an amount that maximizes welfare. Within the scope of price regulation, a definition is provided for price settings or levels, the net benefit for the consumer from consuming the goods or services produced and for the owner of the monopoly in the form of present and future returns. Finding a “fair price” should be the highest maxim for this. In practice, finding this price most often takes place as a part of a negotiation process between the monopolist and the regulatory agency, for which the distribution of negotiation power appears to be of central importance. The prices or profit authorized by the regulatory agency must permit the monopolist to cover the variable and fixed costs. Moreover, based on the principle of opportunity costs, the monopolist must be entitled to a return on the capital employed, in order to compensate for investments or to have an incentive for those type of investments. The challenge lies with the regulatory agency to assess fair interest calculations for the monopolist’s capital employed. The rate of the financing costs allowed has a direct influence on the monopolist’s cash flow. Should a business valuation be conducted on the basis of cash flow, discounting the expected cash flow is a must. The equivalence of the finance cost rate allowed by the regulatory agency and the “actual” finance costs rate of the monopolist appears to be ensured only in an ideal situation – from which positive and negative consequences arise for the M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_1, # Springer-Verlag Berlin Heidelberg 2010

1

2

1 Introduction

company value. Differences in amounts of interest occur, especially for calculating a return on equity, which are due to the diametrically opposing goals of the regulatory agency and the monopolist.

1.1

Purpose of the Dissertation

The problem described above about potentially differing calculations of financing costs is the starting point of this dissertation. It should be shown how one performs a calculation of financing costs, in order to be able to make a claim about “a future orientation”. The present dissertation concentrates on the aspect of considering expected changes in risk, to which the price regulated company is exposed, contingent upon the change of regulation parameters or a shift in the regulatory system. Answering this set of questions will be done using the example of the Austrian electric and gas network industry. Besides the relevance of determining the cost of equity capital for future-based business valuations as discount interest, so the cost of equity capital carries a further, central significance during business valuation for regulated companies as to the prognosis of future cash flow (cash flow as the future profit amount to be discounted). Since a future orientation is essential for both valuating the company as well as establishing the cost of equity, answering this question about how the regulatory system shift changes the company’s cost of equity effected thereby, is the core of this dissertation. Since this set of problems will become more significant in the future in light of efficiency considerations, the principal agent theory should be presented to the reader.

1.2

Structure of the Dissertation

To calculate an appropriate return on equity, the Capital Asset Pricing Model (CAPM) will be explained in Chap. 2. This chapter demonstrates how model parameters should be adjusted, in order to be able to determine more precise projected values. In conjunction with this, this chapter demonstrates how the costs of equity assessed by the regulatory agency have a direct influence on the cash flow rate, within the scope of regulated price settings. This chapter shows premises within which capital cost calculation must move, in order to be able to claim economical correctness when it comes to determining appropriate operating costs. From this, a range for projecting future cash flows should be able to be defined when conducting business valuations based on cash flow. In Chap. 3 an account is given as to what advantages the transition from costbased to price-based regulatory systems offers, both for regulation agencies and for

1.2 Structure of the Dissertation

3

regulated companies. These advantages are derived by applying the principle agent theory. The advantages of this type of change are contrasted with the theoretically possible interpretation of this type of change in the capital market, with the “regulatory lag effect” and the “buffering effect” in Sect. 3.3. Moreover, based on empirical results from the USA, significant criteria are established as to the capital market’s evaluation of a regulatory system as positive or negative. The secondary data analysis from previously published empirical studies on capital market reaction to changes of regulation parameters forms the content of Chap. 4. The methods applied in the primary studies to calculate abnormal returns are explained, in order to analyze the effect of these types of parameter changes on the stock prices of regulated companies in the USA and in Great Britain. By doing this, it should be possible to identify possible significant differences between the different regulatory systems. Simultaneously, international experience is integrated into the present dissertation from the results of Chap. 4. In Chap. 5 an investigation is conducted as to whether international, empirical findings on the effect of regulatory system shifts, as presented in Chap. 4, are also identifiable within the scope of Austrian regulation policies. This takes place in the course of a structural break analysis in Sect. 5.2 with the example of the ¨ sterreichische Elektrizit€atswirtschafts-AG (shorthand: Verbund) and the EVN O AG (shorthand: EVN). It should be determined from this structure fracture analysis whether introducing an incentive regulation for the electricity industry changed the risk and return structure of both securities. Furthermore, an event study is conducted in Sect. 5.3, in order to analyze the stock price reaction of stock from Verbund and from EVN upon the publication of information in the course of the change of regulation parameters for electric and gas network industries. As a part of the event study, different model specifications will be applied to calculate abnormal returns, in order to quantify the influences, based on the models, on the determined rate of abnormal returns. By this, influences based on the models should be able to be considered, especially for the interpretation of the results and general information should be gained such as how sensitive results from event studies react to different model specifications. Chapter 6 serves as the summary of the results from this work as well as the statement about which areas appear meaningful for other works.

Chapter 2

Capital Market-Based Calculation of the Cost of Equity

2.1

Introduction

To conduct cash flow-based business valuations, the projection of future cash flow is necessary. The future cash flow is to be evaluated by means of an appropriate discount interest calculation. For regulated companies, determining an appropriate interest rate has effects on the rate of the discount interest calculation and on the volume of the projected future cash flow. This double meaning from determining the interest rate is the peculiarity for companies whose prices are regulated compared with companies that are active in competitive markets. For planning future profits in price-regulated companies, interest costs for borrowed funds and equity must be considered. As a part of the cash flow-based business valuation, it depends on the valuation model applied whether a return on equity (return required by the investor) or a weighted average cost of capital (WACC) is used as the discount interest calculation. Common to all cash flow-based procedures is that the rate of return is contained in the discount interest calculation. To calculate the return on equity, the capital asset pricing model (CAPM) will be explained more carefully in Sect. 2.2 and the advantages and disadvantages are portrayed. In Sect. 2.3 a normative statement is provided as to how operating costs and capital costs should be made for projecting future cash flow within the scope of a regulatory system, in order to do justice to business requirements. This should enable a founded projection of future cash flow. Section 2.4 serves as the summary of this chapter.

2.2

Capital Market-Based Calculation of the Cost of Equity

By applying capital market-based models to calculate equity costs, risk premiums, which investors require for taking on risks, are derived from capital market data and are not the result of a subjective estimate. For this purpose, reference is most often M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_2, # Springer-Verlag Berlin Heidelberg 2010

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6

2 Capital Market-Based Calculation of the Cost of Equity

made to CAPM in practice, which is a so-called one-factor model. CAPM defines the investor’s return requirements on the basis of a risk-free interest rate, which is increased according to a risk premium. This risk premium is determined by multiplying the market risk premium with the so-called beta factor, as a measurement for the systematic risk of a security. The Arbitrage Pricing Theory (APT) developed by Ross1 is classified as a multifactor model. With APT, risk premium is established by several factors. APT assumes, in contrast to CAPM, that the risk of a security is contingent on an unknown amount of factors in a linear fashion. These factors can be, e.g., exchange rates, interest levels and index trends in various stock exchanges.2 An exceptional form of APT is presented in the FF3F model by Fama and French. This model defines the investor’s return requirements on the basis of 3 factors and hence is classified among the group of multi-factor models.3 In the following, CAPM is explained, on which basis the consideration of risk factors for assessing a risk premium is presented in detail.

2.2.1

Capital Asset Pricing Model (CAPM)

Building on the portfolio theory from Markowitz4 and the separation theory from Tobin,5 the CAPM was developed primarily by Sharpe (1964).6,7 CAPM defines the investor’s return requirements as follows:8 EðRi Þ ¼ Rf þ bi ½EðRm Þ Rf with: EðRi Þ Rf bi EðRm Þ

1

Expected returns from the risky security i Returns from a risk-free capital investment (risk-free interest rate) Measurement for the systematic risk of security i (beta factor) Expected return from the market portfolios

cf. Ross (1976). cf. Mandl and Rabel (1997), p. 310; Buckley et al. (2000), p. 283 ff. 3 cf. Fama and French (1992). 4 cf. Markowitz (1952). 5 cf. Tobin (1957). 6 cf. Sharpe (1964); along with Sharpe, CAPM goes back to Lintner, Treynor and Mossin as well. 7 cf. Damodaran (2001), p. 164 f. 8 cf. Mandl and Rabel (1997), p. 290; Copeland et al. (2002), p. 265; Fischer (2002), p. 74; Drukarczyk (2001), p. 354. 2

2.2 Capital Market-Based Calculation of the Cost of Equity

7

Security Market Line (SML) 17.5% 15.0%

Rendite

12.5% m 10.0% 7.5% 5.0% 2.5% 0.0% 0

0.5

1 Beta

1.5

2

Fig. 2.1 Security market line. “m” in the figure represents the situation of the market portfolios, which intrinsically has a beta factor of 1

This linear interrelationship between expected volume of the investor’s return requirement and the systematic risk is depicted graphically as the Security Market Line in Fig. 2.1.9 When determining risk premium, CAPM assumes the entire risk of a precarious security decomposes into a systematic and an unsystematic part. The unsystematic risk is not influenced by the capital market, rather it is influenced from factors that are evaluated as specific to a security. These could be, e.g., certain characteristics of the management or the client structure. These factors can be diversified through portfolio formation, which is why the capital market does not compensate for unsystematic risk components.10 Systematic risk components cannot be avoided through diversification, which is why they are compensated from the capital market.11 Systematic components are generally e.g., tax policy measures, economic and interest trends.12 Moreover, the original form of CAPM is based on further restrictive premises:13 l l

9

The planning horizon is one period. All investors are unwilling to take risks (risk aversion).

cf. among others Fischer (2002), p. 75; Mandl and Rabel (1997), p. 290; Spremann (2006), p. 310; Copeland et al. (2002), p. 265; Franke and Hax (2004), p. 353. 10 cf. Mandl and Rabel (1997), p. 290; Fischer (2002), p. 74, 103; Copeland et al. (2002), p. 265; Damodaran (2001), p. 155 ff. 11 cf. Spremann (2006), p. 314 f. 12 cf. Mandl and Rabel (1997), p. 290 f.; Purtscher (2006), p. 108. 13 cf. Mandl and Rabel (1997), p. 291; Ballwieser (2002), p. 738; Fischer (2002), p. 71 f.; Damodaran (2001), p. 164.

8


l

All investors have homogeneous expectations. All risky securities are traded on the capital market and can be divided in any way. Funds can be received or invested without restriction at a risk-free interest rate. There are no limitations, transaction costs or taxes. All information is available to the investor at no charge (information efficiency). The prices of risky securities are not influenced by an investor’s purchase or sales activities.

l

l l l l

Further developments in the original CAPM have nullified several of these premises (partially).14 The original form of CAPM is the basis of the investigations in this work. The planning horizon of one period is especially a problematic assumption for determining capital costs for a business valuation because business valuations very often imply an infinite planning horizon. However, if the cost of equity rate determined in accordance with CAPM is applied for a longer period of time, stationary conditions are implied. The parameters defining returns are to be assumed as constant for the entire period under consideration.15 How individual parameters from CAPM are determined for defining the cost of equity rate is shown in the following.

2.2.1.1

Risk-Free Interest Rate

The risk-free interest rate can only be determined approximately. In practice, long term, fixed-interest bearing securities are offered by debtors with very good solvency (e.g., long term state debentures), for which equivalence in the term and in the planning horizons between the company’s expected holdings and the interest maturity are of special importance for reasons of comparison.16 An orientation to actual rate of return on state bonds with a term of 10–30 years is often recommended, whereas for Austrian securities with a very long term, infrequent disbursement, low liquidity and the increased sensitivity to inflation rates thwart the advantage of the approximated matching maturities. A practicable alternative is to draw on the approx. 10-year government bonds and to apply the returns to long term government bonds, published monthly from the Austrian National Bank.17 Fixing the risk-free interest rate should essentially occur on the basis of the future. Consequently, the historical interest rates would be discarded. However, since estimating future interest rates is only possible with great uncertainty, the 14 cf. Brennan (1971); Black (1972); Merton (1973); Rubinstein (1976); Lucas (1978); Breeden (1979); Hansen and Richard (1987); Overviews on this by: Rudolph (1979) and Copeland and Weston (1988), among others. 15 cf. Fama (1977), p. 7 ff. 16 cf. Ballwieser (2002), p. 737; Purtscher (2006), p. 109. 17 cf. Purtscher (2006), p. 109.


9

alternative also exists to use current rates from government bonds. For this, it is assumed that the current returns on these types of securities are the best estimates for future returns.18 Government bonds must provide evidence for cash in any currency that was implemented for the calculation of the expected cash flow of the company to be valuated. Otherwise, there would be a currency exchange risk, which would complicate the comparison of profit with cash flow from alternative transactions.19 Implementation of the above is recommended for small return differences between short and long term, risk-free securities. However, should the yield curve, which displays the interrelation between rate of return and maturity, not show a flat structure, period-specific interest rates can also be applied as an alternative to applying a uniform, risk-free interest rate. This indeed increases the calculation effort for the costs of equity, but it leads to more consistent results.20

2.2.1.2

Market Risk Premium

Market risk premium is calculated as the difference between return from the market portfolio and the risk-free interest rate, on the basis of historical return. The amount of this premium is dependent upon the risk-free interest rate chosen as well as the calculation period; both the risk-free interest rate and the return from the market portfolio are not constant throughout the time period. To avoid inconsistencies when determining market risk premium, the risk-free interest rates that must be used are those that were fixed for the interest of risk-free alternative investments in CAPM.21 Further, it is important to take note that the determination of market risk premium, as the difference between return from the market portfolio and the riskfree interest rate, can only be approximated by applying the appropriate market indices. The decision for or against the use of arithmetic or geometric averages has a significant influence on the rate of the market risk premium determined, bearing in mind that arithmetic averages for return fluctuations end up higher than geometric averages.22 Copeland, Koller and Murrin assume that the actual market risk premiums lies between the geometric and the arithmetic mean.23 Subsequently, the market risk premium is to be checked as to whether the expected future market trends can be described as plausible from this. Provided

18

cf. Ballwieser (2002), p. 738; Busse and Colbe (2002), p. 7; Copeland et al. (2002), p. 266. cf. Ballwieser (2002), p. 737. 20 cf. Daske and Gebhardt (2006), p. 531; Mandl and Rabel (2006), p. 104 f. 21 cf. Purtscher (2006), p. 109. 22 cf. Ballwieser (2002), p. 739; Purtscher (2006), p. 109. 23 cf. Copeland et al. (2002), p. 271. 19

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that this is not the case, the historical returns thus do not give the best estimate for future trends; an appropriate adjustment of the market risk premium is to be made.24

2.2.1.3

Beta Factor

In CAPM, consideration of company-specific or project-specific systematic risk for defining the required rate of return on equity occurs via the beta factor. This factor measures the change of the (historical) individual rate of return from the security with the change of the (historical) market rate of return.25 The beta factor represents the quotient of the covariance between the rate of return of the security i and the rate of return on the market portfolio m and the variance of the rate of return of the market portfolio:26 bi ¼ with: bi CovðRi ; Rm Þ s2m

CovðRi ; Rm Þ s2m

Beta factor from company i Covariance of security return i and market return m Variance in the market return m

A beta factor of 1, determined according to its structure and in accordance with the method of the least square estimate, means that the rate of return of the security develops proportionally to the market rate of return. A beta factor of >1 means that the rate of return of the security strongly reacts disproportionately to market fluctuations in relationship to the market rate of return, and thus displays stronger price fluctuations than the market portfolio and for this reason, a higher rate of return on equity is required in this scenario as a compensation for taking on an increased risk. Conversely, if the beta factor is less than 1, this leads to a reduction of the required rate of return on equity because price fluctuations for this security are lower in comparison with the market portfolio and thus this security presents a lower risk. A beta factor of 0 presents a risk-free assessment, for which reason the risk-free interest rate corresponds to the return on equity requirements in this scenario.27 24

cf. Maier (2001), p. 299; Daske and Gebhardt (2006), p. 531; It must be especially noted that the data used does not include events such as wars or currency reforms, as long as these events are not expected in the future. 25 When using historical returns to calculate the beta factor, one is bound by the following assumption: ex-ante probability distribution ¼ ex-post probability distribution, stochastically independent of the realization of returns, stationary process of returns generation within one period; cf. among others, Maier (2001), p. 300. 26 cf. Mandl and Rabel (1997), p. 297; Fischer (2002), p. 75. 27 cf. Fischer (2002), p. 74; Mandl and Rabel (1997), p. 297.


11

As noted above, measuring the beta factor most often takes place on the basis of historical rate of return. However, since business valuation is oriented toward the future, the representativeness of a beta factor determined on the basis of historical market trends should be reviewed for the future.28 Systematic risk can be decomposed into two fundamental component parts:29 l l

Operating Risk Financial Risk

On the basis of this type of fundamental decomposition of the beta factor based on historical data and only statistically presented, it should be possible to increase the future reference of the beta factor by means of an effective hypothesis.

2.2.1.3.1

Operating Risk

Operating risk contains any systematic risk factors that are shaped predominantly through the industry in which the respective company is active.30 The profit cycle of a company is defined by its belonging to an industry. This cycle can be strongly or less strongly shaped and can correspond to the general market cycle or exhibit an acyclical trend compared with the market index. The amount of the beta factor is influenced by the strength of the cyclicality. Here the company with a stronger cyclicality tends to exhibit a higher beta factor than does a company with a lower cyclicality.31 However, substantial, expected changes of the operating risk can only be taken into account in a simplified manner.32

2.2.1.3.2

Financial Risk

The financial risk is contingent upon the level of debt of the company in question because it is assumed that the risk for the investor increases with increased financing from borrowed funds.33 This effect is weakened from the tax-related

28 cf. Mandl and Rabel (1997), p. 306; Purtscher (2006), p. 111; Knieps (2003), p. 1000; Maier (2001), p. 299. 29 cf. Mandl and Rabel (1997), p. 299. 30 cf. Mandl and Rabel (1997), p. 299. 31 cf. Buckley et al. (2000), p. 311; Spremann (2006), p. 344 f.; Born (1995), p. 151 f.; Nielsen (1992), p. 228 ff.; Mandl and Rabel (1997), p. 306. 32 cf. Mandl and Rabel (1997), p. 306. 33 cf. Fischer (2002), p. 129 f.; Buckley et al. (2000), p. 313 ff.; Drukarczyk (2001), p. 357.

12


consideration of interest on borrowed funds. The interrelation between the level of debt and the indebted or debt-free beta factor is presented formally as follows:34 h i bv ¼ bu 1 þ ð1 sÞ FK bf ð1 sÞ FK EK EK with: bv bu s FK EK bf

Beta factor from the indebted company Beta factor from the debt-free company Corporate tax rate Market value of the borrowed funds Market value of the equity Beta factor of the borrowed funds

Provided that the investor’s rate of return requirement (rðFKÞ) does not correspond to the risk-free interest rate (Rf ), and hence the beta factor of the borrowed funds is greater than 0, then the beta factor for the borrowed funds can be determined from the following equation with an appropriate conversion:35 rðFKÞ ¼ Rf þ bf ½EðRm Þ ir

2.2.1.3.3

Other Influencing Factors

Besides the influence of operating and financial risks on the beta factor, other potential influencing factors are to be accounted.36 The highly condensed information on the effect of influencing factors listed in Table 2.1 can only be understood as a very rough directional indicator because the basic, underlying empirical studies have produced different results and an unequivocal cause-effect interrelation is not demonstrable. Table 2.1 Influencing factors on the beta factor level Influencing factor Characterized by Disbursement behavior High disbursement rates Growth Large growth Company size Bigger company Degree of diversification High degree of diversification Market power Significant market power Liquidity High liquidity a BF beta factor.

34

cf. Mandl and Rabel (1997), p. 299 f.; Fischer (2002), p. 126. cf. Mandl and Rabel (1997), p. 300. 36 cf. Hachmeister (2000), p. 217 ff. 35

Effecta Index for lower BF Index for higher BF Index for lower BF – Index for lower BF Index for lower BF


2.2.1.4

13

A CAPM Evaluation

In the following, difficulties with defining model parameters necessary for CAPM are discussed and possible problems when implementing CAPM in empirical papers are explained.

2.2.1.4.1

Efficiency and Definition of Market Portfolio

As outlined above, the market portfolio is formed by a market index, as the sum of all risk-laden investment possibilities. As for the validity of the assumption of a linear relationship between the systematic risk of a security and the average rate of return from the market, the choice of the market portfolio is of utmost importance for an empirical review.37 Roll (1977) and Roll and Ross (1994) have demonstrated that the linear interrelation between systematic risk and the average market rate of return is not a given, if the market portfolio chosen is inefficiently diversified in comparison with a theoretically ascertainable, overall “investment universe”.38 Stambaugh (1982) points out how sensitively CAPM tests react to different definitions of the market portfolio. Oertmann and Zimmermann (1996) examined the effects of different specifications of the market portfolio on the level of the betas determined for stock from credit institutions. They arrived at the following result, which underscores the leverage of the choice of the market portfolio, as shown in Table 2.2.39 Spremann (2006) adds to the reasons why empirical reviews of CAPM evaluate this as inaccurate by including the possibility that investors make irrational Table 2.2 Sensitivity of the beta factor with change in the market portfolio specification

37

Country Switzerland Switzerland Switzerland Germany Germany Germany French French French England England England

Enterprise

Beta MSCI-country UBS 1.095 SBC 0.863 CS 1.279 Deutsche Bank 0.901 Dresdner Bank 0.751 Commerzbank 0.895 Paribas 1.451 Societe Generale 0.999 BNP 0.946 Barclays 1.316 Nat West 1.386 Lloyds Bank 1.125

Beta MSCI-world 0.889 0.703 0.944 0.535 0.479 0.572 1.034 0.751 0.717 0.925 0.972 0.688

cf. Laux (2003), p. 208. The theoretical validity of CAPM for additional consideration of an investment possibility not yet contained in the market index is shown by Spremann (2006), p. 324 ff. 39 cf. Oertmann and Zimmermann (1996), p. 276. 38

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decisions. He grounds this in the restricted possibility of acting rationally or in the fact that investors make portfolios from more complex investment decisions than those assumed by Markowitz’s portfolio theory.40

2.2.1.4.2

Anomalies

Already in the 1980s empirical investigations came to the conclusion that expectations of the rate of return on the basis of CAPM systematically deviate from the actual, observable expectations of the rate of return. Investigations on the interrelationship of company growth potential, measured for instance by price earning ratios and their rate of return on equity, showed that securities with low growth potential exhibit a positive, risk-adjusted rate of return. This effect has been coined as the “value effect” and was recognized by Basu (1977).41 The “book to market effect” was recognized by Stattmann (1980). This anomaly describes the interrelation between the ratios of equity book values to equity market value with equity returns. As long as the ratio between book value and market value of equity is high, a higher rate of return is expected.42 Banz (1981) recognized that small companies exhibit a positive, risk-adjusted rate of return. This effect of market capitalization of a security is called “size effect”.43 Empirical studies also came to the conclusion that temporary anomalies exist. Stocks in January and on certain weekdays, show significant, positive, risk-adjusted rate of returns, as Fama (1991) shows, among others.44 Fama and French have especially examined “size effect” and “value effect” in a detailed manner. For the period between 1963 and 1990, Fama and French established the average monthly rate of return for approx. 1,000 US stocks on the basis of ten categories for company size and ten categories for the beta value determined. The result from Fama and French’s investigation is summarized in Table 2.3.45 As Table 2.3 shows, the average rate of return for companies with similar market capitalization hardly changes on the different beta levels. On the basis of the assumption of CAPM, this could not be the case because CAPM assumes a linear interrelation between rate of return on equity and the beta factor. However, the average rate of return for companies with identical beta factors on their market capitalization changes in a way that the average rate of return drops with increased 40

cf. Spremann (2006), p. 334 ff.; Spremann (2007), p. 456 f. cf. Basu (1977); Reinganum (1981); Sharpe et al. (1993). 42 cf. Spremann (2007), p. 461. 43 cf. among others Hung et al. (2004), p. 89; Spremann (2007), p. 459. 44 An overview on works confirming this effect is given by Spremann (2006), p. 338 f. 45 cf. Fama and French (1992), p. 434; an overview on the works from Fama/French in the 1990s is given by, among others: Spremann (2007), pp 462–464; Spremann (2006), p. 341 ff.; Franke and Hax (2004), p. 357; Ziegler et al. (2007), p. 359 ff.; Wallmeier (2000), p. 32 ff. 41

2.2 Capital Market-Based Calculation of the Cost of Equity Table 2.3 Beta factors according to size categories Average Beta-low 2 3 4 Average 1.3 1.3 1.3 1.4 1.3 Small 1.5 1.7 1.6 1.8 1.6 2 1.3 1.3 1.4 1.4 1.4 3 1.2 1.1 1.3 1.2 1.7 4 1.3 1.3 1.1 1.5 1.1 5 1.3 1.3 1.4 1.4 1.5 6 1.2 1.1 1.5 1.3 1.2 7 1.1 1.0 1.2 1.3 1.1 8 1.1 1.1 1.1 1.4 1.2 9 1.0 1.0 0.9 1.0 1.1 Big 0.9 1.0 0.9 1.1 0.9

5 1.3 1.5 1.7 1.3 1.3 1.4 1.2 1.2 1.3 1.1 0.9

15

6 1.3 1.5 1.6 1.1 1.1 1.2 1.2 1.1 1.0 1.2 0.9

7 1.2 1.4 1.4 1.3 1.4 1.1 1.2 1.2 1.2 0.9 1.0

8 1.2 1.6 1.3 1.4 1.2 1.3 1.0 0.6 1.0 0.8 0.7

9 1.3 1.5 1.3 1.3 1.4 1.2 1.1 1.3 1.0 0.9 0.7

Beta-high 1.1 1.4 1.1 0.8 1.0 1.1 1.0 0.8 0.9 0.6 0.6

market capitalization. It can be derived from this that the beta factor does not provide an explanation for the average rate of return, however, market capitalization appears to have a significant influence on this. The findings from Fama and French were refuted by several authors.46 The basic question, as is formulated by Roll (1977), is whether the validity of CAPM is even possible because the market portfolio can only be approximated for this by implementing a market index as a proxy variable and any empirical CAPM test can only be a test for the market index, regardless of whether this corresponds to the market portfolio.47 However, this point of criticism overlooks the empirically fixed, systematic interrelation between rate of return deviations and certain figures, as presented by Fama and French.48

2.2.1.4.3

Estimate and Specification Problems When Determining Beta

When determining the beta factor based on historical market data, various problems arise which reduce the quality of the beta factor. Determining the beta factor essentially is based on a linear equation, which is also described as a market model: Rit ¼ ai þ bi Rmt þ uit with: Rit Security returns in period t Rmt Returns from the market portfolios in period t ai The constant from the regression line bi Slope of the regression line (beta factor) uit Confounding variable from the regression model for security i in period t 46

cf. Damodaran (2001), p. 173 f. cf. Roll (1977); Damodaran (2001), p. 172; Spremann (2006), p. 331 ff.; Wallmeier (2000), p. 34. 48 cf. Wallmeier (2000), p. 34. 47

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This market model is based on four premises: 1. The expected value of the confounding variable is 0. 2. The variance of the confounding variable is constant over time. 3. The confounding variables from two periods that follow each other are not correlated. 4. The confounding variable has a normal distribution. Violating one or more of these premises as well as additional peculiarities and specification problems can adversely effect the quality of a beta factor determined according to the method presented above. These causes are explained as follows:

Heteroscedasticity The property that the variances of a confounding variable change over time is called heteroscedasticity. This can be evoked by a falsely assumed linear interrelationship or be based on strong time-related trends of the variable.49 The implication is that the beta factor determined is no longer efficient because the regression line determined no longer minimizes the confounding variable.50

Autocorrelation of the Confounding Variable Autocorrelation of the confounding variable is present if the previously observed values of the confounding variable exert a systematic influence on the following observation values. A systematic trend of this type can be caused by not considering a significant parameter in the regression line or can be based on a falsely assumed linear interrelation.51 Additionally, in the case of autocorrelation from observation values from the confounding variable, the beta factor determined is no longer efficient.

Autocorrelation of Security Returns Provided that the security returns to be estimated themselves exhibit the effect of autocorrelation, the estimate of a beta factor according to the OLS method52 leads to a distorted and inconsistent value determination.53

49

cf. Zimmermann (1997), p. 62. cf. Ulschmid (1994), p. 210. 51 cf. Becker (2000), p. 39. 52 The form known in German as the “method of least squares” for determining regression lines is described as an “OLS method”. 53 cf. Becker (2000), p. 41; Hachmeister (2000), p. 194. 50


17

Measurement Errors If securities possess low liquidity or do not react synchronously with the market index to new information relevant to market value, by which the assumption of an efficient capital market is violated, this leads to a distorted and inconsistent beta factor.54

Choice of Market Index The beta factor portrays the upward slope of the regression line, in relationship to the market index used. By choosing the market index, against which the security returns should be recovered according to the OLS method, the amount of the beta factor is influenced. Reference is made here to the explanations at Sect. 2.2.1.4.1.

Estimate Period Lengths Beta factors are not constant over time. If the interval of time for determining beta is extended, this leads to a higher quality of the regression lines. However, this causes an allowance of anachronistic market data for determining beta and contradicts the basic future orientation.55 It has been substantiated empirically that the beta factor sinks when extending the estimate period because strong, short term fluctuations can balance out this factor.56 In practice, an interval of 1 year is defined for determining the beta factor. For cyclical values, at least one cycle should be completely incorporated into the interval.57

Definition of Return Interval The definition of a return interval is also important for determining the beta factor. The return interval defines any period on which the calculation of a security return is based. In a normal scenario, this could be daily, weekly or monthly returns. Frantzmann and Pfennig, among others, substantiated this empirically on the German stock market. On the basis of their investigations, they came to the conclusion that the extent of the return interval has an increasing influence on the beta factor.58

54

cf. Becker (2000), p. 42 f. cf. Mandl and Rabel (1997), p. 297 f. 56 cf. Hachmeister (2000), p. 197. 57 cf. Timmreck (2002), p. 302; Becker (2000), p. 51. 58 cf. Frantzmann (1990), p. 71; Pfennig (1993), p. 17. 55

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2.2.1.4.4

Determining Adjusted Beta Factors

As portrayed in Sect. 2.2.1.4.3, beta factors determined on the basis of historical market data might not meet the theoretical requirements for various reasons. Furthermore, a beta factor determined by the OLS method was exclusively determined by historical data and thus brackets the future orientation. However, should the cost of equity be determined for the future, the beta factor must be reviewed in a continuous manner as to whether or not the data basis can be used as the best estimate for future trends.59 These deficiencies should be corrected by adjusting the historical beta factor. Different procedures can be applied for this type of adjustment, of which the following are explained:60 l l l l

Mean value method The blume procedure The MLPFS procedure Vasicek procedure

Mean Value Method Any adjustment procedure is identified as a mean value method when the procedure is applied to large data providers such as Barra or Bloomberg. This method assumes that beta factors have the intrinsic tendency during the time lapse to converge on the beta factor of the market portfolio, which is 1. For this reason, the following adjustment is made in the mean value method, in order to minimize the effect of underestimation and overestimation: badj ¼ with: badj bhist bM

2 1 b þ bM 3 hist 3

Adjusted beta factor following the mean value method Historical beta factor before adjustment Beta factor from the market portfolios ¼ 1

Pedell (2007) writes about this: “. . . When estimating beta factors, gearing towards past data is particularly problematic because significant structural breaks regarding risk can result for companies whose fees are regulated, precisely from changes in regulation itself. The estimated beta factors are thus to be interpreted carefully and adjusted, if necessary, especially for changes in the regulation mechanism. These type of adjustments require a foundation from theoretical and empirical knowledge about the determination of the risk in regulated comanies. . . .”, Pedell (2007), p. 47. Please note, that this is a translation (German). 60 It is noted in advance that there are no plausible reasons for the inherent preference of one of these procedures compared with other procedures in general. cf. Pfennig (1993), p. 23. 59


19

The Blume Procedure The procedure developed by Blume accounts for the determination of the beta factor for one period, whose value arises from the previous period:61 bit ¼ at þ bt bi;t1 with: bit bi;t1

Realized beta factor from security i in period t Realized beta factor from security i in period t 1

The values determined for a and b on the basis of this equation are assumed to be constant. However, this assumption cannot be substantiated empirically.62 Nevertheless, the estimate precision can be increased on the basis of this method.63 The MLPFS Procedure This procedure developed by the investment bank Merril Lynch Pierce Fenner and Smith accounts for the interrelation of the beta factor from two periods by means of the correlation coefficient of the time-dependent beta factor as follows:64 bit ¼ 1 þ rt ðbi;t1 1Þ with: bit bi;t1 rt

Realized beta factor from security i in period t Realized beta factor from security i in period t 1 Correlation coefficient betweenbi;t and bi;t1

If the correlation coefficient takes on a value of 0, this means that there is no interrelation between the beta factor from the time period t and t 1. The best estimate for the beta factor for t is thus displayed in the beta factor from the market portfolio, which is 1. Otherwise, if the correlation coefficient takes on a value unequal to 0, this shows the interrelation between the beta factor of previous periods and the current periods. The Vasicek Procedure This procedure defined by Vasicek on the basis of the Bayes theorem accounts for the degree of imprecision of the beta estimate from previous periods for determining the most efficient beta estimate from the current period. This transpires 61

cf. Blume (1971). cf. Zimmermann (1997), p. 246. 63 cf. Ulschmid (1994), p. 248. 64 cf. Hachmeister (2000), p. 187. 62

20


by considering the security-specific beta factor and the average beta factor as follows:65 bi;tþ1 ¼ with: bi;tþ1 bm;t bi;t Varðbi;t Þ Varðbt Þ

Varðbi;t Þ Varðbt Þ bm;t þ bi;t Varðbi;t Þ þ Varðbt Þ Varðbi;t Þ þ Varðbt Þ

Estimated value of the beta factor for security i in period t þ 1 Average beta factor in period t Realized beta factor in period t Security-specific variance of the beta factor in period t Variance of all beta factor in period t

By weighing the security-specific beta factor and the average beta factor with the respective variance of the factor to be weighted, the estimate precision is improved.66 In accordance with the above equation, security-specific beta factor estimates that are relatively reliable in the previous periods are considered stronger in the weighted average than the average beta factor and vice versa.

2.2.2

Conclusion

The capital market-based calculation of the cost of equity by means of CAPM is convincing because of the model’s plain intelligibility as well as because of the theoretical foundation of the interrelation between the expected rate of return and systematic risk. The theoretical foundation, however, is based on restrictive premises, which would essentially limit the possible applications of this model in practice. CAPM’s parameters are only definable empirically by applying the proxy variables. How sensitive the results from CAPM react to the application of various indices as proxy for the market portfolio was demonstrated above. In practice, CAPM is used readily, not least because of its (apparent) simple, didactic usability. Empirical studies prove that the prognosis quality of capital market rate of returns is low with CAPM. By adjusting the beta factor, one can attempt to increase the prognosis quality. In whatever form this type of adjustment should be made, it is not formulated in general terms, however, and this type of adjustment often lacks a theoretical point of reference. Any adjustments are to be substantiated on an individual basis.

65

cf. Hachmeister (2000), p. 187 f.; Ulschmid (1994), p. 252. cf. Schultz and Zimmermann (1989), p. 201; Zimmermann (1997), p. 249.

66

2.3 Rate of Return on Equity as a Regulatory Parameter

2.3 2.3.1

21

Rate of Return on Equity as a Regulatory Parameter Introduction

In market-based systems, attempts are essentially made to arrive at price formations by means of the effects of supply and demand in markets. For certain goods, however, this type of price formation mechanism is not possible or is only hardly possible, for which reason official regulatory measures become necessary. Besides public goods such as national defense, included in this are goods that have an enormous investment cost, which leads to their being no appeal for companies to become active in that type of market. These type of natural monopolies are, e.g., electric, gas and water networks as well as railway and road infrastructures.67 In the following, possible goals from official regulatory measures are presented, in order to emphasize price regulation related to them. The following shows how fair prices are determined in regulated industries and what meaning that has for the cost of equity in regulated companies. By considering these economical principles, it should be possible to produce a grounded prognosis regarding future revenue for price-regulated companies, in order to derive the necessary cash flow volume within the scope of a cash flow-based business valuation convincingly.

2.3.2

Goals in Regulation

The legislator, i.e., the regulatory agency wants to achieve certain goals by regulating industries. These can be (1) control of market power and guidelines for (2) quality and the extent of the provision of goods and also certain (3) social goals. The first group of goals includes the prevention of abuse from monopoly power and anticompetitive behavior. The second group. Is directly oriented toward the consumer. A basic, sufficient provision of goods should be ensured and minimum qualities of service provision are defined. The third group attempts to protect the interests of socially weaker people (e.g., retirees, the handicapped and the sick) or certain groups (e.g., agricultural communities).68

2.3.3

Defining Fair Prices

In order to avoid overloading the state’s budget, it can be assumed as a rule that price-regulated companies have prices, i.e., profits approved that ensure the 67

cf. K€upper (2002), p. 31; Ko¨nig and Benz (1997), p. 70 ff.; Geradin et al. (2005), p. 25 ff. cf. K€upper (2002), p. 32 f.; taken from: Broomwich and Vass (Broomwich and Vass (2002)), Sp. 1678. 68

22


self-financing of the company in question. In order to attain this, the company must have prices approved that can cover both the operating costs as well as the financing costs.69 Operating costs are to be understood as the variable costs (e.g., material costs, external services etc.). Due to capital consumption, financing costs are effected in the form of depreciation and the cost of interest.

2.3.3.1

Operating Costs

Accounting for operating costs as a prognosis parameter for fair prices, i.e., profits can take place by using actual costs or budget costs. Whereas actual costs can be verified to a great extent upon application, this is not the case for budget costs, which can be verified upon application only to a lesser extent compared with actual costs. Bearing in mind the requirement of legality when setting fair prices, applying actual costs seems to be expedient. However, in addition to legality, the aspect of efficiently providing a service is considered, in which the consideration of objectives in the form of budget costs and target costs seem to be expedient. Much attention must be given to the generally unattainable operationalization of the efficiency concept to attain an equilibrium between legality and efficiency orientation when defining fair prices.70

2.3.3.2

Financing Costs

Due to capital consumption, financing costs are effected in the form of depreciation and the cost of interest.71 The cost of interest can be subdivided into the costs for borrowed funds and the costs for equity. Swoboda defines three principles that should apply for defining fair financing costs:72 Principle 1. “The investor’s expected rate of return from the EVU should be fair for the capital market and it should also correspond to the special risk of the investor”. Principle 2. “The costs should be distributed fairly across the consumer’s various periods”. Principle 3. “The costs that underlie pricing are to be determined in such a way that negative incentives can be avoided”. Principle 1 is derived from the goals in regulation presented above. In order to prevent companies from attaining a monopoly income, the basic principles of pricing in markets with full competition must be considered when setting fair prices. For this reason, investors must be entitled to a rate of return on invested equity 69

cf. K€upper (2002), p. 34; taken from: Broomwich and Vass (2002), Sp. 1679. cf. K€upper (2002), p. 33 f. 71 cf. Knieps (2003), p. 994; Seicht (2001), p. 105 ff. and 115 ff. 72 Swoboda (1990), p. 66 ff. Please note that this is a translation (German). 70


23

appropriate to the risk. Setting a higher or lower rate of return should be avoided within the scope of setting prices.73 Principle 2 is justified by the relatively long life of investments in the network infrastructures, compared with other branches of the economy, and the possibility of a very different periodization of cash flow. Besides the life of the investment, the consideration of future expenditures in the form of accruals has a significant influence on the setting of fair prices (e.g., accruals for pension).74 Principle 3 contains the requirement that fair prices should not be set on the basis of (historical) actual costs because this does not provide an incentive for the management of a regulated company to organize service provision in an efficient manner.75 As has already be portrayed above on setting fair operating costs, the consideration of budget costs instead of actual costs is a possibility for providing an incentive to provide services efficiently. As a part of setting fair financing costs, this principle can be realized, for instance, by checking the investment costs for large projects if the investment costs that are classified as too high may not be passed on to the customer as financing costs.76

2.3.3.2.1

Depreciation

When determining fair depreciation, the depreciation period applied and the depreciation method applied must be reviewed. Before this, it must be defined whether historical and initial costs, production costs, current price or the projected replacement costs should be applied for setting the depreciation to be accounted for.77 Historical Costs and Current Prices or Replacement Costs Answering the question about the economically correct accounting of capital consumption in the form of depreciation can only be answered when bearing in mind the set of questions about the fair rate of return for companies that have an intrinsic right of monopoly. This question is answered by means of the target definition for the regulatory system, especially by means of the definition in which form the costs to be approved must be determined. In the 1990s in Austria, the basic objectives of price regulation in the electricity industry were based in finding “economically justified prices”.78 Whether setting “economically justified prices” is to be understood in the sense of the lowest prices possible or whether there is interpretive leeway for considering “actual” profits (excess returns), is 73

cf. Swoboda (1990), p. 67. cf. Swoboda (1990), p. 67 f.; Swoboda (1992), p. 84. 75 cf. Swoboda (1990), p. 68. 76 cf. Swoboda (1990), p. 68. 77 cf. Swoboda (1990), p. 69. 78 cf. Seicht (1996), p. 345; Mayer (2002), p. 197. 74

24


ultimately a political question. It must be noted with certainty, however, that excess returns are not attainable in markets with full competition, but rather the investors only receive interest appropriate to the risk for the capital invested. In this respect, an orientation to pricing premises with full competition seems to be the only correct basis for further investigations.79 Swoboda, representing an orientation to historical costs and production costs, argues his position as follows:80 . . .Investments can be self-financed, externally financed or a combination of both. To the extent that they are externally financed, obvious depreciation of the cost price and of the nominal interest calculated from the respective book values are sufficient to satisfy the claims of the lender. Depreciation of more than 100% of the investment or a higher interest settlement would end in profits for the lender, for which no initial investment accounts. This would stand in contrast to a competition situation. Analogously, this is also valid for the self-financed part of the investment. Investors expect a return from their assets that is appropriate to the risk. This type of expected return is enabled by means of pricing, in which the calculation incorporates depreciation of cost price (which could be used for repayment of principal) and interest, including an appropriate risk premium from each of the book values. That depreciation and a part of the targeted return on equity are not disbursed cannot be used as a counter argument. The depreciation compensated as well as the retained profit can be invested in assets that again justify a fair return. . . .

However, Swoboda grants that under the following condition the application of replacement prices would lead to the same result as does the orientation to historical costs:81 . . .The inflation rate, toward which the nominal interest rate is adjusted, must be exactly the same in the increase of the replacement price, in order to maintain the real interest rate. . . .

If this condition postulated by Swoboda is not fulfilled, an orientation to replacement prices would lead to positive or negative excess returns. To qualify this, it must be mentioned that real capital maintenance is not possible if taxation of collected compensation due to inflation is involved. Nevertheless, this is the case if nominal capital costs are passed on to the customer on the basis of the book value of historical costs and production costs.82 On the contrary, Seicht argues that sheer orientation to replacement price can guarantee the regulated company a long-term maintenance of asset value. He bases this postulate of asset value maintenance on the company’s mandate to supply a good, which he interprets as a service duty. This mandate to supply a good precludes a change of industry or the liquidation of a price-regulated company, for which reason the assurance of asset value maintenance must be the highest goal.83

79

Even the ordinance on charges for system use stipulates for prices to be allowed that these “. . . are to be determined based on costs . . .”, cf. Mayer (2002), p. 197. 80 Swoboda (1996), p. 365. Please note that this is a translation (German). 81 Swoboda (1996), p. 364. Please note that this is a translation (German). 82 cf. Swoboda (1992), p. 83; Swoboda (1996), p. 366. 83 cf. Seicht (1996), p. 351 ff.


25

Seicht grants, however, that only a real interest rate is to be used to calculate financing costs when orienting to replacement prices.84

Depreciation Period Principle 2 presented above requires the application of the service life when calculating depreciation, which in the ideal situation corresponds to the technical service life of the fixed assets. Since the technical service life does not only depend on the demands of the assets, but also on the maintenance and replacement policies, applying the service life defined outside of operation seems to be hardly possible.85 It must be ensured that 100% of the depreciation must be passed on to the customer even if the technical and operational service lives differ from each other.86

Depreciation Method Besides linear methods of depreciation, digressive or progressive procedures can also be applied. Bearing in mind Swoboda’s principle 2 presented above, the straight-line methods appear to correspond to this, although the financing costs exhibit a digressive trend and hence the sum total of the capital costs are lower over time because the book value decreases as the basis for setting financing costs. On an international level, straight-line depreciation has prevailed.87

2.3.3.2.2

Financing Costs

To calculate fair financing costs, a calculatory approach can be implemented, which reflects the subjective view, or a market-based approach, which reflects the view of the capital market. Both approaches together provide the answer to specific detailed questions, which are presented in the following.

A Basis for Calculating Interest The basis for determining financing costs is formed by means of a balance sheet view of the assets or capital, corrected for certain adjustments. These adjustments relate on one hand to the assets that are necessary for operation because, in general, only assets that are necessary for operation are considered as a basis for financing costs. A further criterion is formed by whether the assets are interest-bearing. 84

cf. Seicht (1996), p. 355. cf. Swoboda (1990), p. 70. 86 cf. Swoboda (1990), p. 70; Swoboda (1996), p. 372 ff. 87 cf. Swoboda (1990), p. 71. 85

26


Assets that are necessary for operation form the basis for interest calculation only to the extent that no interest-bearing equity items account for them. Noninterest related equity items are understood as contributions to building costs and government grants, for instance.88 Concerning accruals for pension, it is important to consider whether the interest claims from employees eligible for benefits are designated in personnel expenditures or in financial income. As long as these claims are designated in personnel expenditures and hence are a component part of operating costs, accruals for pension may not be calculated into the basis of calculation for interest because this would mean a double calculation of these interest claims. If the latter is the case, the accruals for pension are described as borrowed funds that are eligible for interest.89 For supplier’s accounts payable, interest settlement can arise in the form of guaranteed discounts. As long as services including discounts are activated and flow into the price calculation in the form of depreciation, including supplier’s accounts payable in the basis of interest would mean a double charge of interest from the interest expenditure to the customer.90 The question about applying historical cost items or daily prices when determining depreciation also has effects when defining the basis of calculation for interest. As long as historical costs are used to calculate depreciation, it only seems consistent to apply the balance sheet book value to calculate the capital employed on the basis of historical costs. The same applies in an analogous form for applying current prices or future replacement prices when calculating depreciation.

Calculating Interest Rates – The Calculatory Approach The calculatory approach denotes the concept of determining an appropriate rate of capital costs as summarized in cost accounting under the concept calculatory interest. Methodologically this is reached by raising a risk-free interest rate to a subjectively guaranteed risk surcharge. When using nominal interest rates, the basis for calculating interest is the continued historical costs and production costs. As long as real interest rates are used, the basis for calculating interest is formed by the balance sheet assets evaluated at the replacement price minus the interest-free borrowed funds.91 The basic problem of the calculatory approach, the subjective establishment of a risk surcharge, should be overcome by the capital market-based approach.92

88

cf. Swoboda (1990), p. 71. cf. Busse and Colbe (2002), p. 9. 90 cf. Busse and Colbe (2002), p. 9. 91 cf. Busse and Colbe (2002), p. 4. 92 cf. Swoboda (1996), p. 376. 89


27

Calculating Interest Rates – The Capital Market-Based Approach The capital market-based approach defines the interest rate when determining financing costs by taking account of the return requirements from the self-financiers and external investors. The risk-free interest rates serve as the basis for both the return requirements from the self-financier as well as for those of the external investor. These interest rates are increased for specific risk surcharges that are determined on the basis of capital market models. Besides CAPM, as the capital model used most often, there are also other models available.93 Reference is made here to the explanations in Sect. 2.2 of this work.

2.3.4

Conclusion

For projecting future cash flow surplus in price-regulated companies, the regulatory system defining profit or price must be considered. From this, a range can be determined for the amounts of future cash flows. The expected profits from regulated companies correspond with the allowed costs that account for them. These costs can be separated into operating costs and capital costs. Whereas for the operating costs specific assumptions must be made regarding their acceptance in the regulatory system applied, budgeting of the capital costs is possible on the basis of basic economical reflection, as long as it can be expected that the regulatory agencies also are oriented to these principles. The following can be maintained for incoming payments that correspond to capital costs: 1. Depreciation To calculate future cash flow, which serves as the cover for capital consumption in the form of depreciation, it must be taken into consideration whether the depreciation is calculated assuming historical costs or on the basis of replacement values. Allocating compensation due to inflation either to depreciation or to financing costs must be considered when calculating financing costs. The service life underlying the depreciation determination as well as the depreciation method used (straight-line, progressive, digressive) must comply with the principle of “fair distribution to the generations”, unless the regulatory agencies themselves deviate from this principle. 2. Financing costs Interest-bearing assets that are necessary for operation minus non-interest bearing capital items available should apply as the capital basis to calculate financing costs. The rate of capital costs related to this capital basis should be calculated by

93

cf. Swoboda (1996), p. 376 f.

28


applying a capital market model. The calculatory derivation of a rate of capital costs does not have to be performed. A rate determined for financing costs must include a risk surcharge, for which the amount depends on “. . . what risks a price regulation leaves to the power supply company”.94

2.4

Conclusion

In Sect. 2.2 CAPM and the set of problems related to a basic orientation to the past were presented. By adjusting parameters in the model, attempts can be made to impute a higher-value future orientation to the cost of equity rates determined. However, these adjustments take place most often without the theoretical or empirical foundation required by them. The analysis of which economical principles should be considered regarding the projection of future cash flow in regulated companies was presented in Sect. 2.3. This analysis showed that “fair” operating costs and appropriately determined capital costs, which contain a fair risk premium, are to be acknowledged by the regulatory agency and should be significant for regulating “fair” prices.

94

Swoboda (1996), p. 377.

Chapter 3

Methods of Price Regulation

3.1

Introduction

There are essentially two methods for defining fair prices in regulated industries. The first method, known as rate of return regulation, routinely fixes prices in the amount of the actual costs.1,2 The second method, known as RPI-X regulation, defines prices on the basis of a price or profit formula.3 In this case, the price of the current period consists of the price of the previous period subtracted by the mandatory efficiency boosts that take the form of price surcharges and price increases, in order to compensate for the general price escalation.4 In the following, both of these different systems will be defined more carefully. On the basis of the principle agent theory, a statement is made in Sect. 3.2 about which different incentive effects both of these systems have intrinsically for boosting efficiency in providing a service.

3.1.1

Rate of Return Regulation

“Rate of return regulation (abbreviated: ROR Regulation) denotes a regulatory system that fixes prices for products or services for a period on the basis of a 1

Synonyms used for this type of price regulation are also “individual cost audit system”, “costbased price regulation” and “Return regulation”. 2 For a rate of return regulation system, actual costs do not have to be used in practice for fixing prices; budget costs or target costs can also be implemented; cf. Sect. 2.3.3 of this work. For reasons of simplification, implementing actual costs should be assumed, in order to simplify the difference between rate of return regulation systems and RPI-X regulation systems. 3 Synonyms used for this type of price regulation are also “Revenue cap regulation”, “Price cap regulation” and “multiple-period incentive regulation”. 4 The expression “RPI-X regulation” is derived from both of the factors listed. “RPI” stands for the factor related to price increase from inflation (Retail-Price-Index) and “X” for the factor related to efficiency increase.

M. Hierzenberger, Price Regulation and Risk, Lecture Notes in Economics and Mathematical Systems 641, DOI 10.1007/978-3-642-12047-3_3, # Springer-Verlag Berlin Heidelberg 2010

29

30

3 Methods of Price Regulation

regulatory agency’s auditing results on costs reported by price-regulated companies. The definition of these prices is made by a regulatory agency that is authorized by the state for this purpose. It should be assumed that a company’s total costs are the subject of the cost audit. Furthermore, it should be assumed that the company audited possesses hidden information on the composition of their total costs compared with the regulatory agency, which does not know the actual composition of the total costs, but can only make an inference about the actual composition by means of the report issued about this composition by the company. It should not be ruled out that the costs reported correspond to the actual costs. In this type of system, the risk incurred from business activity by a company is compensated only in the form of an appropriate capital return. Profits that go beyond a fair capital return are not permitted.

3.1.2

RPI-X Regulation

The term RPI-X regulation should be understood as a system for defining prices in natural monopolies that fixes prices for products without ever considering the costs that underlie the production of goods or service provision. Thus, it is possible for price-regulated companies to generate profits that go beyond a capital return permitted. Losses are also possible in this type of price regulatory system because the cost-covering principle in ROR regulation is no longer the basis for fixing prices. By isolating price trends from cost trends, an incentive should be created for a company over the course of time to make use of its efficiency potential, in order to generate profits. Price trends can be affected directly by a price-cap regulation or indirectly by a revenue-cap regulation.

3.2

The Principal Agent Theory

The goal of Sect. 3.2 is to highlight reasons for the transition between cost-based and price-based regulatory systems and to illustrate what economical consequences justify this type of transition for regulated companies as well as for regulatory agencies. These statements are made on the basis of the principal agent theory. In Sect. 3.2.1, a principal agent model is outlined5 that describes the situation of an ROR regulation with complete information. In connection therewith, the same section shows how the situation with incomplete information creates incentives for the price-regulated company not to report costs accurately in this type of regulatory system for the purpose of increasing company profit. Following this is the outline of what decisions price-regulated companies come to regarding the use of possible efficiency increase potential, both assuming the existing efficiency potential and 5

An introduction to the principal agent theory is given by Macho-Stadler and Perez-Castrillo (2001), among others.

3.2 The Principal Agent Theory

31

assuming the decision about (new) production technology. All the explanations in Sect. 3.2.1 are based on the assumption of an ROR regulatory system. Section 3.2.2 highlights how the results from Sect. 3.2.1 change when assuming an RPI-X regulatory system. Section 3.2.3 serves as the summary of Sect. 3.2.

3.2.1

Rate of Return Regulation

3.2.1.1

A Solution for Complete Information

The basic function of an ROR regulatory system should be clarified in the following model, which derives from Laffont and Tirole:6 A company that is subject to an ROR regulatory system produces a good and possesses the following total cost function C ¼ ðy eÞ q þ a k e þ e with: C e y q a K e

Total costs Effort/Employment of labor (adjustment for hardship) Efficiency Output Fixed costs Technology Random variable

This function illustrates that the total costs are dependent upon the volume produced, in which the costs/unit are dependent upon the employment of labor used by the company or management. The company’s fixed costs are independent of the volume produced; however, these fixed costs depend on the choice of (production) technology. The parameter e possesses an expected value of 0 and for this reason can be ignored for further explanations. The good produced generates a benefit for consumers of the good in the form of SðqÞ, with S0 > 0 and S00 < 0. This means that the consumer’s total benefit increases with the boost in the volume produced (and consumed); however, the marginal utility decreases with the increase of the volume consumed. It should further be assumed that the management of the company receives a fixed payment in the amount of t from the state or the regulatory agency for their service rendered. In order to be able to pay the t to the management, it is valid to assume that taxation is necessary for this and the increase of t is charged to the consumer in the form of a tax increase. 6

cf. Laffont and Tirole (1986), p. 614 ff.

32


The management’s benefit is defined in the form UM ¼ Et dðeÞ and dðeÞ represents any disutility from work provided on the management’s side. It should be assumed that this disutility rises with the increase of labor services (d0 ðeÞ > 0) and also that the marginal disutility is increasing (d00 ðeÞ > 0). The consumer’s net benefit from the regulated company’s business activities can be stated as follows: UK ¼ SðqÞ ð1 þ lÞ Eðt þ CÞ In this net benefit function, the factor ð1 þ lÞ should depict the issue presented above that the service of t to the company’s management is related to tax collections and thus, exhibits a disadvantage for the consumer, which increases by means of the increase of t. Thus, the following maximization problem results, as long as e as well as the other parameters of the cost function are observable: maxfSðqÞ ð1 þ lÞEðt þ CÞ þ UM g

ðq;e;tÞ

¼ fSðqÞ ð1 þ lÞ½dðeÞ þ ðy eÞq lUM g With the restriction of the following participation condition: UM 0 This condition formally illustrates that a solution of the maximization problem is only possible on the condition that the company’s management at least targets a benefit of 0 from the business activity. Otherwise, participation for management would not be meaningful for logical reasons. The first-order conditions from the maximization problem presented above are: UM ¼ 0 S0 ðqÞ ¼ ð1 þ lÞðy eÞ d 0 ðeÞ ¼ q The participation condition is binding and the marginal utility from one consumer unit (S0 ðqÞ) corresponds to the company’s marginal costs. Furthermore, the marginal disutility for the management of the labor services corresponds to the marginal utility achieved from it for the consumer, which corresponds to the reduction of marginal costs.

3.2.1.2

Incomplete Information: Cost Report Reimbursement

As presented above, incoming payments that a company receives from fulfilling the production tasks are dependent upon the total costs reported, (CR ), among other


33

things. If the total costs reported deviate from the actual total costs (CT ) and the total costs reported are “acknowledged” by the regulatory agency, this means a profit improvement for the company (CT < CR ), in contrast to a report issued about the actual total costs, and vice versa. It is thus assumed, following Currier (2004),7 the regulatory agency knows the actual cost function of the company, except for one parameter. This parameter is the productivity parameter y. The cost function CT is defined by b and the volume produced q, with y ¼ ðy1 ; :::; ym Þ as the vector. y can also be written in a simplified manner as y ¼ ðyi ; yi Þ. In this case, yi stands for all possible values of y, with the exception of yi . The cost function CT ðy; qÞ hence, corresponds to CT ðyi ; yi ; qÞ. It is further assumed that the regulatory agency is aware of yi and the company would like to maximize utility by reporting yi and is ready to attain this by means of issuing a false report. The parameter k should depict this formally with k > 0. The company reports kyi . Unless k ¼ 1 is applicable, the costs reported correspond to the actual costs. The cost function that is made known to the regulatory agency, thus has the following form: CR ðkyi ; yi ; qÞ. By choosing the parameter k, it should become possible for the company to maximize profits. Both the company and the regulatory agency are aware of the demand function pða; qÞ, with a ¼ ða1 ; :::; an Þ as the vector. The economic consequences of the assumptions and definitions outlined should be demonstrated by the example of an average cost regulatory system.8 The following maximization problem arises for the company:9 max pða; qÞq CT ðy; qÞ on the condition: pða; qÞ ¼ ACR ðkyi ; yi ; qÞ with: ACR ¼

CR q

In order to solve this maximization problem, the following langrange function is to be generated: Lðk; q; lÞ ¼ pða; qÞq CT ðy; qÞ þ l pða; qÞ ACR ðkyi ; yi ; qÞ 7

cf. Currier (2004), p. 51 ff. Currier (2004) shows analogously in his work for a marginal cost regulatory system, cf. Currier (2004), p. 51 f. A marginal cost regulatory system is different by an additional consideration of a payment s to the firm, which is paid by the state or the regulatory agency to cover fixed costs. 9 cf. Currier (2004), p. 53 f. 8

34


The necessary, first-order conditions are thus: Lk ¼ l ACRkbi yi ¼ 0 Lq ¼ p þ qpq CTq þ lpq ACRq ¼ 0 Ll ¼ pða; qÞ ACR ðkyi ; yi ; qÞ ¼ 0 Since yi 6¼ 0 and ACRkbi 6¼ 0 are the case, l ¼ 0 is applicable. For this reason, the marginal profit corresponds to the marginal costs MR ¼ MCT because: p þ qpq ¼ CTq Hence, it can be summarized that for an average cost regulatory system and a false report, a company achieves a profit and chooses a production volume (q), as if without regulation. Thus, the regulated company has an incentive to notify the regulatory agency of increased costs. 3.2.1.3

Incomplete Information: Efficiency Boosts

As presented above, fixing prices in cost-based regulatory systems occurs on the basis of costs made known. Through this, companies have no incentive to lower costs as long as the regulatory agency acknowledges the costs made known and fixes prices on this basis. The following shows what consequences this can have when realizing possible efficiency boosts potential through pre-existing technology as well as through new production technology. 3.2.1.3.1

A Model with Pre-existing Technology

Following Braeutigam and Panzar (1989),10 assume a company produces two goods: G1 and G2 . G1 describes the product that is offered in a regulated market and G2 describes each product that is offered on an unprotected market. To produce both of these goods, fix costs F are incurred that cannot be directly allocated to a product (e.g. overhead costs). The variable unit costs, however, can be allocated directly to G1 and G2 . The question thus arises, how F should be allocated to both goods. Relative quantities such as revenue, variable unit costs or output volumes act as reference parameters for this type of allocation. This type of allocation function for G1 is presented formally as follows: Relative output volume: f ðG1 ; G2 Þ ¼ ðG1 Gþ1 G2 Þ Relative unit costs: f ðG1 ; G2 Þ ¼ ðc1 cþ1 c2 Þ Relative revenue: f ðG1 ; G2 Þ ¼ R1 ðGR11ÞðGþ1pÞ2 G2 10

cf. Braeutigam and Panzar (1989), p. 373 ff.


35

The function f ðG1 ; G2 Þ takes on a value between 0 and 1 and the following is applicable: f1

@f >0 @G1

f2

@f 0 is the case, the first derivation of L following e1 would be exactly zero. However, this is only possible as shown above if l ¼ 1 is the case. If, however, it were applicable that the condition l ¼ 1 is met, then the first derivation of L following G1 equals f1 F > 0. This is not possible for maximum development.11 Hence, it can be held that: @L=@e1 < 0 l 5%

Wiener Stadtwerke (Province Vienna) > 10% EVN AG > 10%

Stockholder structure EVN free float < 14%

Energie Baden Württemberg AG > 35%

NÖ LandesBeteiligungsholding GmbH (Province Lower Austria) 51%

Fig. 5.2 Stockholder structure for Verbund and EVN (cf. Annual Reports from Verbund and EVN for the 2008 fiscal year)

companies in accordance with IFRS for the period between December 2002 and September 2008 (cf. Fig. 5.4).6 During this period, both companies demonstrate a relatively constant or slightly falling tendency in the level of indebtedness. For this reason, the changes in the

6

The values presented are book values and should serve as an appropriate estimation for the market values, especially because of the valuation in accordance with IFRS. The interrelation between level of indebtedness and the beta factor was explained in Sect. 2.2.1.3.2 in Chap. 2 of this work.

5.1 Hypotheses and Database

75 Market trend: ATX / Verbund / EVN

Index: 1.1.2003 = 100 %

800.0% 700.0% 600.0% 500.0% 400.0% 300.0% 200.0% 100.0%

01 .0 1. 01 03 .0 4 01 .03 .0 7 01 .03 .1 0. 01 03 .0 1 01 .04 .0 4. 01 04 .07 . 01 04 .1 0. 01 04 .01 . 01 05 .0 4. 01 05 .0 7 01 .05 .1 0 01 .05 .0 1 01 .06 .0 4. 01 06 .07 . 01 06 .1 0. 01 06 .0 1 01 .07 .0 4 01 .07 .0 7.0 01 7 .1 0 01 .07 .01 . 01 08 .0 4.0 01 8 .0 7. 08

0.0%

Time ATX

Verbund

EVN

Fig. 5.3 Market trend from ATX, Verbund and EVN(01/2003 – 06/2008) (Stock prices were made available from Verbund’s Investor Relations Department. The prices relayed were compared with those from the database http://de.finance.yahoo.com/ (14 Feb. 2009). No deviations were established. Stock splits and dividend disbursements were taken into consideration) Level of indebtedness: Verbund and EVN 90.0% Level of indebtedness

80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 06.08

09.08

03.08

12.07

09.07

06.07

03.07

09.06

12.06

06.06

03.06

12.05

06.05

09.05

03.05

12.04

09.04

06.04

03.04

12.03

06.03

09.03

03.03

12.02

0.0%

Quarterly report Verbund

EVN

Fig. 5.4 Level of indebtedness for Verbund and EVN (12/2002–09/2008)

level of indebtedness should be disregarded for the investigations in this chapter and the hypotheses tests should occur on the basis of the beta factor that was established empirically.7 7

A lowering of the level of indebtedness tends to lead to a lower risk for investors. Since the investigations of this work involve increasing risk amounts, not considering this effect should contribute to an addition, if small, strengthening of potentially significant results. As to the

76

5 The Primary Empirical Study

The definition of a proxy size for the market trend is also necessary for the empirical study implementing a market model. For this, three indices are used: the “ATX”, the “Dow Jones Stoxx 600” and the “Dow Jones Stoxx Global 1800”. The “Dow Jones Stoxx 600 Utilities” is used as a benchmark or a control portfolio for Verbund and EVN. In the following, the configuration of the indices listed is explained.8 (1) Austrian Traded Index (ATX) The ATX, as the most significant stock index from the Vienna Stock Exchange, is composed of the 20 biggest corporations on the Vienna Stock Exchange. According to Ultimo 2008, the ATX is made of the weights of 20 values displayed in Table 5.6, with a weight factor of approx. 10.1% for Verbund.9 (2) Dow Jones Stoxx 600 (DJ600) The DJ600 is a stock market exchange indices in which the 600 largest European corporations are included.10 Table 5.6 Composition of the ATX on 31 December 2008 Code Unternehmen EBS ERSTE GROUP BANK AG TKA TELEKOM AUSTRIA AG OMV OMV AG VER VERBUNDGESELLSCHAFT AG VOE VOESTALPINE AG VIG VIENNA INSURANCE GROUP RIB RAIFFEISEN INT. BANK-HLDG AG ICL INTERCELL AG WIE WIENERBERGER AG PST OESTERR. POST AG ANDR ANDRITZ AG MMK MAYR-MELNHOF KARTON AG FLU FLUGHAFEN WIEN AG STR STRABAG SE BWIN BWIN INT. ENTERT. AG RHI RHI AG SBO SCHOELLER-BLECKMANN AG PAL PALFINGER AG ZAG ZUMTOBEL AG AUA AUSTRIAN AIRLINES AG Sum

ISIN AT0000652011 AT0000720008 AT0000743059 AT0000746409 AT0000937503 AT0000908504 AT0000606306 AT0000612601 AT0000831706 AT0000APOST4 AT0000730007 AT0000938204 AT0000911805 AT000000STR1 AT0000767553 AT0000676903 AT0000946652 AT0000758305 AT0000837307 AT0000620158

Weight (%) 15.9 14.7 11.6 10.1 7.7 6.4 6.2 4.3 4.1 3.5 2.9 2.3 2.1 1.9 1.8 1.3 1.1 0.8 0.8 0.7 100.0

interrelation between level of indebtedness and the beta factor, reference is made to Sect. 2.2.1.3.2 in Chap. 2 of this work. 8 Price indices were used (from Laspeyres). Data from the Dow Jones Indices were taken from http://www.stoxx.com/ (14 Feb. 2009). 9 Data was taken from:http://www.indices.cc/static/cms/sites/indices/media/de/pdf/download/ ultimo/2008/atx_2008.pdf (04 Mar. 2009). 10 cf. http://www.stoxx.com/indices/types/benchmark.html (04 Mar. 2009).

5.1 Hypotheses and Database

77

(3) Dow Jones Stoxx Global 1800 (DJ1800) The DJ1800 is a global stock market exchange indices and includes the 600 largest European, American and Asian corporations.11 (4) Dow Jones Stoxx 600 Utilities (DJ600UTIL) The DJ600UTIL is a subindex of the DJ600 and is made up of the corporations from the utility sectors contained in the DJ600. This index contains 33 companies, including Verbund with a weight of around 0.7%. Besides Verbund, the following 32 values are contained in this index with the weights listed in Table 5.7.12 Table 5.7 Composition of the DJ600UTIL on 31 December 2008

11

Country DE FR DE ES IT GB GB GB FR FR FI PT ES GB GB ES IT IT GB ES ES ES GB ES AT GB IT GR CH PT GB CH IT Sum

Enterprise Weight (%) E.ON 16.1 GDF SUEZ 13.5 RWE 8.9 IBERDROLA 7.6 ENEL 6.9 NATIONAL GRID 6.6 CENTRICA 6.2 SCOTTISH and SOUTHERN ENERGY 4.4 EDF 3.4 VEOLIA ENVIRONNEMENT 2.8 FORTUM 2.4 EDP ENERGIAS DE PORTUGAL 2.1 UNION FENOSA 2.1 INTERNATIONAL POWER 1.7 UNITED UTILITIES GRP 1.6 RED ELECTRICA CORPORATION 1.3 TERNA 1.2 SNAM RETE GAS 1.2 SEVERN TRENT 1.1 IBERDROLA RENOVABLES 1.0 ENAGAS 0.9 GAS NATURAL SDG 0.9 DRAX GRP 0.8 ENDESA 0.8 VERBUND 0.7 PENNON GRP 0.7 A2A 0.7 PUBLIC POWER CORPORATION 0.6 ALPIQ HOLDING REG 0.4 EDP RENOVAVEIS 0.4 NORTHUMBRIAN WATER GRP 0.4 BKW FMB ENERGIE 0.3 HERA 0.2 100.0

cf. http://www.stoxx.com/indices/types/benchmark.html (04 Mar. 2009). Data was taken from:http://www.stoxx.com/indices/download.html?symbol=SX6P

12

78

5 The Primary Empirical Study Market trend: Verbund / EVN / ATX / DJ600UTIL / DJ600 / DJ1800

Index: 1.1.2003 = 100 %

800.0% 700.0% 600.0% 500.0% 400.0% 300.0% 200.0% 100.0% 01 .0 1. 01 03 .0 4. 01 03 .0 7 01 .03 .1 0. 01 03 .0 1 01 .04 .0 4. 01 04 .07 . 01 04 .1 0. 01 04 .0 1 01 .05 .0 4. 01 05 .07 . 01 05 .1 0. 01 05 .0 1 01 .06 .0 4. 01 06 .07 . 01 06 .1 0. 01 06 .0 1 01 .07 .0 4. 01 07 .07 . 01 07 .1 0. 01 07 .01 . 01 08 .0 4. 01 08 .07 .0 8

0.0%

Time Verbund

EVN

DJ600UTIL

ATX

DJ600

DJ1800

Fig. 5.5 Market trend for Verbund, EVN, ATX, DJ600UTIL, DJ600 and DJ1800 (01/2003–06/ 2008)

The market trends between 1 January 2003 and 30 June 2008 from the stocks and indices relevant to the empirical investigation of this chapter are displayed in the graph (cf. Fig. 5.5). The following displays the descriptive statistics from the six stock market quotes, separated according to time periods:13 l

l

01 Jan. 2003 – 30 Jun. 2005: time period before introducing the incentive regulation on electricity 01 Jul. 2005 – 30 Jun. 2008: time period after introducing the incentive regulation on electricity

The time period of the investigation from 01 January 2003 to 30 June 2008 was chosen because the regulatory agency E-Control took over the business activity for electricity in 2001 and for natural gas in 2002, thus ensuring that the basic conditions before the founding of E-Control are not portrayed in the market performances of Verbund and EVN.14 The end of the time frame on 30 June 2008 was selected so as not to incorporate into this investigation the trends in the internal capital markets after the middle or end of 2008 (keywords “financial market crisis” and “real economy crisis”). The descriptive statistics from the database can be seen in Tables 5.8–5.10. For additional investigation of the data, the data compiled was examined at its extreme values. The extreme values determined on the basis of a box plot analysis can be seen in Fig. 5.6 and the respective extreme value tables (cf. Table 5.11).

13

cf. the event list (event A1 “introduction of the incentive regulation”) in Table 5.57, Sect. 6.3.1 of this work. 14 cf. Section 4.2 in Chap. 4 of the present work.

Table 5.9 Descriptive statistics from the database (time period of 01/2003 to 06/2005) Descriptive Statistics 01/2003–06/2005 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 620 0.094 0.042 0.052 0.00173 0.000455 0.011331 EVN 620 0.104 0.044 0.060 0.00067 0.000529 0.013160 DJ600UTIL 620 0.087 0.044 0.043 0.00077 0.000372 0.009267 ATX 620 0.070 0.040 0.030 0.00161 0.000338 0.008427 DJ600 620 0.099 0.041 0.058 0.00057 0.000399 0.009936 DJ1800 620 0.084 0.036 0.048 0.00039 0.000368 0.009152 Valid N (listwise) 620

Table 5.8 Descriptive statistics from the database (time period of 01/2003 to 06/2008) Descriptive Statistics 01/2003–06/2008 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 1,360 0.195 0.109 0.085 0.00158 0.000436 0.016083 EVN 1,360 0.168 0.081 0.087 0.00068 0.000407 0.015011 DJ600UTIL 1,360 0.133 0.071 0.062 0.00064 0.000259 0.009556 ATX 1,360 0.130 0.075 0.055 0.00097 0.000304 0.011228 DJ600 1,360 0.115 0.057 0.058 0.00029 0.000271 0.009979 DJ1800 1,360 0.084 0.036 0.048 0.00015 0.000231 0.008503 Valid N (listwise) 1,360

Variance Statistic 0.000128 0.000173 0.000086 0.000071 0.000099 0.000084

Variance Statistic 0.000259 0.000225 0.000091 0.000126 0.000100 0.000072

Skewness Statistic Std. Error 0.355 0.098 0.480 0.098 0.144 0.098 0.460 0.098 0.104 0.098 0.200 0.098


Kurtosis Statistic Std. Error 2.121 0.196 2.436 0.196 3.097 0.196 2.100 0.196 3.643 0.196 2.555 0.196


5.1 Hypotheses and Database 79

Table 5.10 Descriptive statistics from the database (time period from 07/2005 to 06/2008) Descriptive Statistics 07/2005–06/2008 N Range Minimum Maximum Mean Std. Statistic Statistic Statistic Statistic Statistic Standardfehler Statistic Verbund 740 0.195 0.109 0.085 0.00145 0.000705 0.019185 EVN 740 0.168 0.081 0.087 0.00068 0.000603 0.016411 DJ1600UTIL 740 0.133 0.071 0.062 0.00053 0.000360 0.009796 ATX 740 0.130 0.075 0.055 0.00044 0.000482 0.013105 DJ600 740 0.110 0.057 0.052 0.00006 0.000368 0.010015 DJ1800 740 0.068 0.031 0.037 0.00005 0.000291 0.007920 Valid N (listwise) 740 Variance Statistic 0.000368 0.000269 0.000096 0.000172 0.000100 0.000063



80 5 The Primary Empirical Study

5.2 Empirical Analysis: Structural Break Analysis

81

Boxplot 01/2003 - 06/2008 0.10

0.05

0.00

–0.05

–0.10

–0.15 Verbund

EVN

DJ600UTIL

ATX

DJ600

DJ1800

Fig. 5.6 Graphic representation of the extreme values of the database

The extreme values were not eliminated from the database for the remaining course of the investigation because possible causes of incorrect data compilation were precluded for the extreme values.

5.2

Empirical Analysis: Structural Break Analysis

This section investigates whether introducing the incentive regulation on electricity made a change in the systematic risk for Verbund and EVN. The amount of the systematic risk, measured by the beta factor in the market model, is influenced by various parameter stipulations. Reference is made here to the explanations in 2.2.1.4.3 of this work. In order to take account of the influence of the choice of market index, the investigations take place by applying three different indices for each hypothesis test: the ATX, the DJ600 and the DJ1800. The Index DJ600UTIL serves as the control portfolio. The following regression model is used for the investigation: 0

0

Rit ¼ ai þ ai Ds þ bi Rmt þ bi Ds Rmt þ uit

82


Table 5.11 Extreme values from the database, separated according to time periods 01/2003–06/ 2005 and 07/2005–06/2008 Extreme Values 01/2003–06/2005 (RoR) Extreme Values 07/2005–06/2008 (PC) Nr. Value Nr. Value Verbund Major 1 235 0.052 1 636 0.085 2 264 0.048 2 652 0.072 3 620 0.045 3 648 0.057 4 574 0.045 4 635 0.056 5 86 0.038 5 214 0.054 Least 1 102 0.042 1 224 0.109 2 425 0.035 2 206 0.082 3 467 0.033 3 632 0.077 4 532 0.030 4 231 0.072 5 144 0.028 5 407 0.060 EVN Major 1 613 0.060 1 158 0.087 2 42 0.055 2 130 0.086 3 347 0.052 3 214 0.078 4 254 0.051 4 225 0.069 5 519 0.050 5 51 0.056 Least 1 99 0.044 1 222 0.081 2 72 0.041 2 412 0.061 3 340 0.036 3 634 0.057 4 341 0.034 4 236 0.057 5 90 0.034 5 224 0.057 DJ600UTIL Major 1 50 0.043 1 635 0.062 2 56 0.041 2 162 0.044 3 64 0.034 3 47 0.035 4 1 0.033 4 648 0.032 5 51 0.030 5 712 0.024 Least 1 49 0.044 1 632 0.071 2 57 0.037 2 634 0.049 3 532 0.032 3 412 0.031 4 38 0.030 4 220 0.030 5 62 0.028 5 626 0.027 ATX Major 1 343 0.030 1 635 0.055 2 103 0.028 2 223 0.048 3 83 0.027 3 225 0.040 4 305 0.026 4 599 0.039 5 596 0.025 5 551 0.037 Least 1 406 0.040 1 222 0.075 2 336 0.034 2 671 0.052 3 570 0.031 3 527 0.051 4 303 0.028 4 412 0.049 5 102 0.027 5 632 0.048 DJ600 Major 1 50 0.058 1 635 0.052 2 51 0.039 2 672 0.036 3 1 0.038 3 648 0.033 4 67 0.034 4 680 0.033 5 56 0.033 5 675 0.033 Least 1 57 0.041 1 632 0.057 2 62 0.034 2 671 0.046 3 17 0.033 3 527 0.036 (continued)


83

Table 5.11 (continued) Extreme Values 01/2003–06/2005 (RoR) Extreme Values 07/2005–06/2008 (PC) Nr. Value Nr. Value 4 49 0.032 4 643 0.032 5 94 0.032 5 524 0.031 DJ1800 Major 1 50 0.048 1 680 0.037 2 1 0.039 2 672 0.032 3 52 0.035 3 599 0.026 4 64 0.034 4 702 0.025 5 126 0.030 5 667 0.024 Least 1 57 0.036 1 671 0.031 2 62 0.031 2 412 0.030 3 94 0.028 3 738 0.029 4 397 0.026 4 725 0.026 5 47 0.026 5 621 0.024 The case numbers cited in the table indicate the case number within the respective partial sample “01/2003-06/2005” and “07/2005-06/2008”. Table 5.12 Structure of models from the structural break analysis

with: Rit ai bi Rmt 0 ai 0 bi Ds 1 uit

Model structure Dependent variable Verbund EVN DJ600UTIL

ATX Model 1a Model 2a Model 3a

Market index DJ600 Model 1b Model 2b Model 3b

DJ1800 Model 1c Model 2c Model 3c

Observed returns from stock i at time t Constant from the regression model from company i Increase of the regression equation (beta factor) from company i Market returns at time t Change of the constant ai if: Ds ¼ 1 Change of the constant bi if: Ds ¼ 1 Dummy variable with value 0 (time period 01/2003 – 06/2005) or (time period 07/2005 – 06/2008) Confounding variable

In total, nine models are used in calculations for the structural break analysis, as displayed above (cf. Table 5.12). The hypotheses mentioned above in 5.1 are tested in the following section with the results from the nine regression analysis, with the additional correlation analysis and with the results from the descriptive statistics (cf. Table 5.13).15 15

When the results from the structural break analysis (Sects. 5.2.1–5.2.6) were verbally explained, the alpha probability of error (p) for the distortion of H0 at p < 0.1% were described as extremely significant. Events with 0.9% p 0.1% are described as highly significant and those with 5.0% p > 0.9% as significant. Defining p takes place in Sect. 5.2 on a two-sided basis. Events with 10.0% p > 5.0% are described as distinctive, in order to underscore the events that would be rated as significant at the 5% level with a one-sided significant test, but would have to be rated as not significant with a two-sided test.

84


Table 5.13 Summary of the hypotheses from the structural break analysis Hypotheses H0: H1: bi;RoR 6¼ bi;PC Hypothesis 5: bi;RoR ¼ bi;PC a0i 6¼ 0 Hypothesis 6: a0i ¼ 0 rRoR;ri ;Rm 6¼ rPC;ri ;Rm Hypothesis 7: rRoR;ri ;Rm ¼ rPC;ri ;Rm Hypothesis 8: s2 Ri ;RoR ¼ s2 Ri ;PC s2 Ri ;RoR 6¼ s2 Ri ;PC mR;i;RoR 6¼ mR;i;PC Hypothesis 9: mR;i;RoR ¼ mR;i;PC Table 5.14 Model summary for Verbund – ATX (1a)

Model

R

Model summary Adj. R2 R2

1a

0.481

0.231

Table 5.15 Coefficient model for Verbund – ATX (1a) Coefficients Model Unstandardized coefficients

1a

(Constant) ATX Dummy_(Constant) Dummy_ATX

B 0.001 0.520 0.000 0.219

Standard Error 0.001 0.067 0.001 0.078

0.229

Standardized coefficients Beta 0.363 0.007 0.132

5.2.1

Systematic Risk and Welfare Effect: Verbund

5.2.1.1

Model 1a: Verbund – ATX

Section 5.2.1–5.2.3 5.2.1–5.2.3 5.2.4 5.2.5 5.2.5

Standard error of the estimate 0.014120

t

Sig.

1.551 7.719 0.292 2.801

0.121 0.000 0.770 0.005

A highly significant increase in the beta factor was determined for Verbund by introducing the incentive regulation on electricity when using the ATX as proxy for the market trend (p ¼ 0.5%). A significant change in the constant from the market model was not observed (p ¼ 77.0%), for which reason a welfare effect cannot be assumed based on the introduction of the incentive regulation (cf. Tables 5.14 and 5.15). 5.2.1.2

Model 1b: Verbund – DJ600

When applying the DJ600 as proxy for the market return, an extremely significant increase of the beta factor (p < 0.1%) was established. A welfare effect could not be observed (p ¼ 80.7%) (cf. Tables 5.16 and 5.17). 5.2.1.3

Model 1c: Verbund- DJ1800

When implementing the DJ1800, an extremely significant increase (p < 0.1%) of the beta factor was established. A welfare effect could not be observed (p ¼ 81.8%) (cf. Tables 5.18 and 5.19).

5.2 Empirical Analysis: Structural Break Analysis Table 5.16 Model summary for Verbund – DJ600 (1b)

85

Model

R


1b

0.358

0.128

Table 5.17 Coefficient model for Verbund – DJ600 (1b) Coefficients Model Unstandardized coefficients

1b

(Constant) DJ600 Dummy_(Constant) Dummy_DJ600

Table 5.18 Model summary for Verbund – DJ1800 (1c)

B 0.002 0.219 0.000 0.534

Standard error 0.001 0.061 0.001 0.082

1c


0.126


Model

R


1c

0.236

0.056

Table 5.19 Coefficient model for Verbund – DJ1800 (1c) Coefficients Model Unstandardized coefficients B 0.002 0.147 0.000 0.483

Standard error 0.001 0.069 0.001 0.100


t

Sig.

2.657 3.604 0.244 6.504

0.008 0.000 0.807 0.000


0.053


5.2.2

Systematic Risk and Welfare Effect: EVN

5.2.2.1

Model 2a: EVN – ATX

t

Sig.

2.664 2.138 0.230 4.828

0.008 0.033 0.818 0.000

A highly significant increase of the beta factor could be observed when implementing the ATX for EVN (p ¼ 0.3%). A welfare effect could not be established (p ¼ 62.1%) (cf. Tables 5.20 and 5.21). 5.2.2.2

Model 2b: EVN – DJ600

When implementing the DJ600 as the market index, the beta factor from EVN exhibits an extremely significant boost (p < 0.1%) due to the implementation of the incentive regulation. A welfare effect could not be established when implementing the ATX as the market index (p ¼ 90.9%) (cf. Tables 5.22 and 5.23).

86


Table 5.20 Model summary for EVN – ATX (2a)

Model

R


2a

0.420

0.177

Table 5.21 Coefficient model for EVN – ATX (2a) Coefficients Model Unstandardized coefficients

2a


Table 5.22 Model summary for EVN – DJ600 (2b)

B 0.000 0.390 0.000 0.221

Standard error 0.001 0.065 0.001 0.075

2b


5.2.2.3

0.175


Model

R


2b

0.300

0.090

Table 5.23 Coefficient model for EVN – DJ600 (2b) Coefficients Model Unstandardized coefficients B 0.001 0.189 0.000 0.396

Standard error 0.001 0.058 0.001 0.078


t

Sig.

0.076 5.997 0.495 2.928

0.939 0.000 0.621 0.003

0.088



t

Sig.

0.975 3.264 0.114 5.051

0.330 0.001 0.909 0.000

Model 2c: EVN – DJ1800

Even for an approximation of the market index with DJ1800, an extremely significant increase of the beta factor (p < 0.1%) could be established for the EVN stock, for which again no welfare effect could be established from the introduction of the incentive regulation on electricity (p ¼ 91.8%) (cf. Tables 5.24 and 5.25).

5.2.3

Systematic Risk and Welfare Effect: DJ600UTIL

5.2.3.1

Model 3a: DJ600UTIL – ATX

Neither a significant change of the beta factor (p ¼ 14.1%) nor a welfare effect (p ¼ 75.9%) could be established for the DJ600 when applying the ATX as a proxy variable for the market index (cf. Tables 5.26 and 5.27).

5.2 Empirical Analysis: Structural Break Analysis Table 5.24 Model summary for EVN – DJ1800 (2c)

87

Model

R


2c

0.207

0.043

Table 5.25 Coefficient model for EVN – DJ1800 (2c) Coefficients Model Unstandardized coefficients

2c


Table 5.26 Model summary for DJ600UTIL – ATX (3a)

B 0.001 0.115 0.000 0.404

Standard error 0.001 0.065 0.001 0.094


5.2.3.2


Model

R


3a

0.478

0.228

Table 5.27 Coefficient model for DJ600UTIL – ATX (3a) Coefficients Model Unstandardized coefficients

3a

0.041

B 0.000 0.355 0.000 0.068

Standard error 0.000 0.040 0.000 0.047

0.227



t

Sig.

1.059 1.782 0.104 4.295

0.290 0.075 0.918 0.000


t

Sig.

0.581 8.864 0.307 1.472

0.561 0.000 0.759 0.141

Model 3b: DJ600UTIL – DJ600

When approximating market returns through the DJ600, a significant reduction of the beta factor could be observed (p ¼ 4.1%) and again no welfare effect could be established (p ¼ 62.5%) (cf. Tables 5.28 and 5.29).

5.2.3.3

Model 3c: DJ600UTIL – DJ1800

When implementing the DJ1800 as the market index, neither a change in the beta factor (p ¼ 96.1%) nor a welfare effect could be proven (p ¼ 90.4%) (cf. Tables 5.30 and 5.31).

88


Table 5.28 Model summary for DJ600UTIL – DJ600 (3b)

Model

R


3b

0.772

0.595

Table 5.29 Coefficient model for DJ600UTIL – DJ600 (3b) Coefficients Model Unstandardized coefficients B 3b


Table 5.30 Model summary for DJ600UTIL – DJ1800 (3c)

0.000 0.776 0.000 0.068

Standard error 0.000 0.025 0.000 0.033

0.594


Model

R


3c

0.596

0.355

t

Sig.

1.338 31.505 0.489 2.050

0.181 0.000 0.625 0.041


0.354

Table 5.31 Coefficient model for DJ600UTIL – DJ1800 (3c) Coefficients Model Unstandardized coefficients Standardized coefficients B Standard error Beta 3c (Constant) 0.001 0.000 DJ1800 0.671 0.034 0.597 Dummy_(Constant) 0.000 0.000 0.003 Dummy_DJ1800 0.002 0.049 0.001

5.2.4


t

Sig.

1.653 19.887 0.120 0.049

0.099 0.000 0.904 0.961

Unsystematic Risk

As to systematic risk, measured in the form of a correlation coefficient between stock return or index return and the market return, the following results, contingent upon the market return implemented, could be determined for the entire period of investigation from 01/2003 to 06/2008 (cf. Table 5.32). The correlation coefficient between stock return and market return for the time periods 01/2003 – 06/2005 (ROR regulation) and 07/2005 – 06/2008 (RPI-X regulation) can be seen in Tables 5.33 and 5.34. Investigating whether the differences for the correlation coefficients for the time periods 01/2003 – 06/2005 and 06/2005 – 06/2008 are significant, hence to test hypothesis 7 as outlined in 5.1, takes place by applying the Fisher-Z-Transformation


89

Table 5.32 Correlation coefficients as the measure for unsystematic risk (time period from 01/2003 to 06/2008) Correlations 01/2003–06/2008 ATX DJ600 DJ1800 0.318** 0.198** Verbund Pearson correlation 0.476** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 0.270** 0.173** EVN Pearson correlation 0.413** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 0.771** 0.596** DJ1600UTIL Pearson correlation 0.477** Sig. (2-tailed) 0.000 0.000 0.000 N 1,360 1,360 1,360 **Sig. 0.01 (2-tailed)

Table 5.33 Correlation coefficients as the measure for unsystematic risk (time period from 01/2003 to 06/2005) Correlations 01/2003–06/2005 ATX DJ600 DJ1800 0.192** 0.119** Verbund Pearson correlation 0.387** Sig. (2-tailed) 0.000 0.000 0.003 N 620 620 620 0.143** 0.080* EVN Pearson correlation 0.250** Sig. (2-tailed) 0.000 0.000 0.046 N 620 620 620 0.832** 0.663** DJ1600UTIL Pearson correlation 0.323** Sig. (2-tailed) 0.000 0.000 0.000 N 620 620 620 **Sig. 0.01 (2-tailed) *Sig. 0.05 (2-tailed)

Table 5.34 Correlation coefficients as the measure for unsystematic risk (time period from 07/2005 to 06/2008) Correlations 07/2005–06/2008 ATX DJ600 DJ1800 0.393** 0.260** Verbund Pearson correlation 0.505** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 0.357** 0.250** EVN Pearson correlation 0.488** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 0.723** 0.541** DJ1600UTIL Pearson correlation 0.567** Sig. (2-tailed) 0.000 0.000 0.000 N 740 740 740 **Sig. 0.01 (2-tailed)

90


of the correlation coefficients. By doing this, the negatively and positively skewed distribution of the correlation coefficient is transformed into the approximated normal distribution of the Z value, with z as the relevant statistical test regarding the significance of the difference established between two correlation coefficients:16 z¼

Z 1 Z2 sðZ1 Z2 Þ

whereas: Zi ¼

sðZ1 Z2 Þ

1 1 þ ri ln 2 1 ri

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi 1 1 þ ¼ n1 3 n2 3

with: z Statistical test (Fisher0 s z-value) Zi Z-value for correlation coefficient i ri Correlation coefficient i ni Sample size i According to Fisher, this leads to the following z values for the correlation coefficients determined empirically and displayed above (cf. Table 5.35). The critical value for the two-sided test of hypothesis 7 comes to the 5% level 1.96. All z-values lie outside of the acceptance region of H0, which is why this is disregarded. Both the stock from Verbund and the stock from EVN exhibit a significant reduction in the unsystematic risk because the correlation coefficient for the time period 07/2005 – 06/2008 is significantly higher than for the time period 01/2003 – 06/2005, independently of the market index implemented. The trend of the control portfolio DJ600UTIL exhibits an opposite trend when implementing the DJ600 and the DJ1800 as the market index. Only when implementing the ATX as the market index does the control portfolio also move toward a reduction of the unsystematic risk. Table 5.35 Fisher´s z-values of the differences between correlation coefficients

16

cf. Bortz (1999), p. 209 ff.

Fisher´s z-Wert ATX DJ600 DJ1800

Verbund 2.77 4.02 2.67

EVN 5.14 4.32 3.21

DJ600UTIL 5.79 5.14 3.46

5.2 Empirical Analysis: Structural Break Analysis Table 5.36 Averages and standard deviations per stock/index Descriptive statistics Regime N Mean Verbund RoR 620 0.00173 PC 740 0.00145 EVN RoR 620 0.00067 PC 740 0.00068 ATX RoR 620 0.00161 PC 740 0.00044 DJ600UTIL RoR 620 0.00077 PC 740 0.00053 DJ600 RoR 620 0.00057 PC 740 0.00006 DJ1800 RoR 620 0.00039 PC 740 0.00005

5.2.5

91

Std. 0.011331 0.019185 0.013160 0.016411 0.008427 0.013105 0.009267 0.009796 0.009936 0.010015 0.009152 0.007920

Std. error 0.000455 0.000705 0.000529 0.000603 0.000338 0.000482 0.000372 0.000360 0.000399 0.000368 0.000368 0.000291

Total Risk and Return Averages

To verify hypothesis 8, the standard deviations, as a measurement of the total risk, are used for each security, i.e. index for the two time periods examined (cf. Table 5.36). Based on the variance for each stock or index associated with these standard deviations, the Levene test was conducted for variance equality (cf. Table 5.37). A significant increase of the total risk was established for stocks from Verbund and from EVN. The DJ1800 exhibits a significant reduction of the total risk. For the control portfolio DJ600UTIL and DJ600, no significant changes in the total risk could be established, for which reason the H0 cannot be disregarded for these two indices alone. To verify hypothesis 9 as to significant differences in the mean values of daily returns between both time periods examined, a T-test was applied to each stock, i.e. each index (cf. Table 5.37). With the exception of the ATX, there is no H0 that can be disregarded for any stock or any index. With ATX, a significant increase of the daily returns was ascertainable.

5.2.6

Summary of the Structural Break Analysis

As a part of the structural break analysis, the following hypotheses were tested in Sect. 5.2 (cf. Table 5.38). As for hypothesis 5, it is to be held that for the time period of 07/2005 – 06/2008 for Verbund as well as for EVN, a highly significant boost of the beta factor, independent of the market index used, can at least be established. For the control portfolio, only when implementing the DJ600 as a market index could a significant reduction of the systematic risk be established; in the remaining cases, the control

Table 5.37 Results from the Levene test of variance equality and t-tests for mean value equality Independent samples test Levene’s Test t-Test for equality of means for equality of variances F Sig. t df Sig. (2-tailed) Mean Std. error 95% Confidence interval of difference difference the difference Lower Upper Verbund Equal variances assumed 95.989 0.000 0.324 1,358 0.746 0.000284 0.000876 0.001435 0.002002 Equal variances not assumed 0.338 1,228.271 0.736 0.000284 0.000839 0.001363 0.001930 EVN Equal variances assumed 12.677 0.000 0.016 1,358 0.987 0.000013 0.000818 0.001617 0.001590 Equal variances not assumed 0.017 1,355.450 0.987 0.000013 0.000802 0.001587 0.001560 ATX Equal variances assumed 64.208 0.000 1.912 1,358 0.056 0.001168 0.000611 0.000030 0.002366 Equal variances not assumed 1.983 1,277.067 0.048 0.001168 0.000589 0.000013 0.002323 DJ600UTIL Equal variances assumed 0.419 0.518 0.466 1,358 0.641 0.000243 0.000520 0.000778 0.001264 Equal variances not assumed 0.469 1,338.138 0.639 0.000243 0.000518 0.000773 0.001259 DJ600 Equal variances assumed 0.124 0.725 0.951 1,358 0.342 0.000517 0.000543 0.000549 0.001583 Equal variances not assumed 0.952 1,320.131 0.341 0.000517 0.000543 0.000548 0.001582 DJ1800 Equal variances assumed 8.829 0.003 0.943 1,358 0.346 0.000437 0.000463 0.000471 0.001345 Equal variances not assumed 0.932 1,232.920 0.352 0.000437 0.000469 0.000483 0.001357

92 5 The Primary Empirical Study


93

Table 5.38 Summary of the hypotheses tested as a part of the structural break analysis H0 H1 Section bi;RoR 6¼ bi;PC 5.2.1–5.2.3 Hypothesis 5: bi;RoR ¼ bi;PC a0i 6¼ 0 5.2.1–5.2.3 Hypothesis 6: a0i ¼ 0 rRoR;ri ;Rm 6¼ rPC;ri ;Rm 5.2.4 Hypothesis 7: rRoR;ri ;Rm ¼ rPC;ri ;Rm 5.2.5 Hypothesis 8: s2 Ri ;RoR ¼ s2 Ri ;PC s2 Ri ;RoR 6¼ s2 Ri ;PC mR;i;RoR 6¼ mR;i;PC 5.2.5 Hypothesis 9: mR;i;RoR ¼ mR;i;PC Table 5.39 Summary of the results from hypothesis 5

Table 5.40 Summary of the results from hypothesis 6

Table 5.41 Summary of the results from hypothesis 7

Probability of error for a rejected H0: ATX DJ600 DJ1800 Average

Verbund

EVN

DJ600UTIL

0.005

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