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Empirical Secondary Data Analysis

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Price Regulation and Risk

Part of the book series: Lecture Notes in Economics and Mathematical Systems ((LNE,volume 641))

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Abstract

Chapter 4 examines whether decisions from regulatory agencies on future regulation parameters, contingent on the applied regulatory system (ROR-regulation or RPI-X regulation), have significant differences. Each decision to be reached by the regulatory agency or the complexity of the calculation is defined as the regulation parameter. The economic consequences for price-regulated companies resulting from these decisions by the regulatory agency are identified as regulatory risk. Regulatory risk is a component of unsystematic risk. Unsystematic risk is primarily of significance only for investors that are not completely diversified. For investors that are completely diversified, the systematic risk component is not relevant for business valuation.

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Notes

  1. 1.

    cf. Robinson and Taylor (1998), p. 337.

  2. 2.

    cf. Baecker et al. (2007), p. 276.

  3. 3.

    cf. Mandl and Rabel (1997), p. 290.

  4. 4.

    cf. Sect. 2.2.1.4.2 of this work.

  5. 5.

    This statement assumes that regulatory risk is the decisive factor for the amount of unsystematic risk and that the remaining non-systematic risk factors exert no decisive influence.

  6. 6.

    cf. Ballwieser (2002), p. 739; Mandl and Rabel (1997), p. 306; Purtscher (2006), p. 111; Ulschmid (1994), p. 210; on the relevance of considering industry peculiarities for business valuation cf. Drukarczyk and Ernst (2007): “… Industry peculiarities in valuation is thus not an academic game, but rather of decisive, practical relevance. Its meaning is, we presume at least, much higher than the value relevance from the so-called tax shields, which are hardly discussed in the scientific literature at length, although their value influence shrivels with increasing knowledge of interdependency. …,” Drukarczyk and Ernst (2007), p. VIII.

  7. 7.

    Stigler (1971), p. 3.

  8. 8.

    cf. Peltzman (1976), p. 214.

  9. 9.

    cf. Peltzman (1976), p. 212.

  10. 10.

    cf. Peltzman (1976), p. 213.

  11. 11.

    cf. Peltzman (1976), p. 230.

  12. 12.

    Binder and Norton (1999) characterize this as follows: “Unlike the earlier capture (Gray 1940) or public interest theories (Bernstein 1955), the Peltzman model predicts that the regulator will not choose a corner solution (maximize the wealth of one group) unless the other group is completely politically powerless.” Binder and Norton (1999), p. 250.

  13. 13.

    cf. Binder (1998), p. 111.

  14. 14.

    Bowman (1983) cites applications in other areas: the definition of equilibrium models and their improvement by identifying variables that explain the market behavior; cf. Bowman (1983), p. 562.

  15. 15.

    cf. Cox and Portes (1998), p. 282.

  16. 16.

    cf. MacKinlay (1997), p. 13 f.

  17. 17.

    cf. Cox and Portes (1998), p. 286 f.

  18. 18.

    Moreover, other procedural steps are to be established; presenting them would exceed the limits of this work. Reference is made here to Bowman (1983).

  19. 19.

    cf. MacKinlay (1997), p. 19 f.

  20. 20.

    cf. Brown and Warner (1980), p. 207 ff.

  21. 21.

    cf. Binder (1998), p. 117 ff.

  22. 22.

    cf. Cable and Holland (1999), p. 339.

  23. 23.

    cf. MacKinlay (1997), p. 21.

  24. 24.

    cf. Binder (1998), p. 113; MacKinlay (1997), p. 24.

  25. 25.

    cf. Binder (1998), p. 113; MacKinlay (1997), p. 24.

  26. 26.

    cf. Binder (1998), p. 123 f; Teets (1992), p. 278.

  27. 27.

    cf. Binder (1998), p. 124.

  28. 28.

    cf. The groundbreaking work on this by Binder (1985a).

  29. 29.

    cf. Dewan and Ren (2007), p. 9.

  30. 30.

    cf. Dewan and Ren (2007), p. 9.

  31. 31.

    Besides the study results presented here in a compressed, qualitative form, which are not integrated into the quantitative analysis of this work, reference is made to works listed in Appendix 2 that are also not included in the database of this work, in order to give the reader an overview of the current literature related to the set of questions thematized here. The claim to completeness is not made. Special notice should be given to the works of Norton (1985), Davidson et al. (1997), Morana and Sawkins (2000) and Buckland and Fraser (2002).

  32. 32.

    cf. Norton (1988), p. 224; Sect. 3.3 of the present work.

  33. 33.

    Norton (1988), p. 232.

  34. 34.

    cf. Dnes et al. (1998), p. 219.

  35. 35.

    Dnes et al. (1998), p. 221.

  36. 36.

    cf. Borrmann and Finsinger (1999), p. 343; Swoboda (1996), p. 377.

  37. 37.

    cf. Borrmann and Finsinger (1999), p. 415.

  38. 38.

    cf. on this Mandl and Rabel (1997) “… Market value-defining quantities are thus the expected cash flow […] on one hand and the return requirements from the investor operating in the capital market on the other. …,” Mandl and Rabel (1997), p. 18 f.

  39. 39.

    The simplifying assumption of constant cash flow, a constant, risk-free interest rate, a constant market premium and \( \alpha = {{\hbox{R}}_{\rm{f}}} \) are at the basis of the formalization. Furthermore, constant, unreal, complete self-financing is assumed. “Flow to equity” is to be slated as “cashflow”. Reference is made to Mandl and Rabel (1997), p. 367 ff. for a detailed presentation of the “equity approach.”

  40. 40.

    In this work, the 2-sided T-test is generally used to validate hypotheses by mean values or the Levene Test is used to validate hypotheses by spread measures. For forming hypotheses cf. Bortz (1999), Chap. 4. To validate various hypotheses, interrelated hypotheses and for the introduction in variance-analytical methods cf. Bortz (1999), Chaps. 5 and 6 as well as part II. Reference is made to Backhaus et al. (2000), p. 1–69 for detailed explanations on conducting regression analyzes.

  41. 41.

    cf. Fischer (2002), p. 102.

  42. 42.

    cf. among others, Binder and Norton (1999), Buckland and Fraser (2001) and Grout and Zalewska (2006).

  43. 43.

    Grout and Zalewska (2006) investigated the change announced from the RPI-X system into a “profit sharing system” in the electricity network industry in Great Britain in the 1990s. This type of system is identified by the feature that a company shares possible profits or losses from an RPI-X regulation with the consumers. It hence can be identified as a form combining a profitability regulation and an RPI-X regulation. While implementing the CAPM and the Fama French 3-factor model, Grout and Zalewska determined that the announced change led to a significant reduction in systematic risk, stating that: “… Taking the single-factor and three-factor evidence together, the paper shows that the impact on risk of regulatory changes specifically designed to impact on risk is both significant and consistent with theory,” Grout and Zalewska (2006), p. 180.

  44. 44.

    cf. Schwert (1981).

  45. 45.

    cf. Binder (1985b), p. 167 f.

  46. 46.

    Reference is made to Appendix 1 of this work for a detailed presentation of the events examined in the primary studies.

  47. 47.

    The results from the primary studies were integrated in this work in a balanced manner.

  48. 48.

    In this work, logarithmized returns are not computed analogously as done methodologically in the primary studies. cf. Strong (1992), p. 535 for applying logarithmized returns.

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    Dr. Michael Hierzenberger

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Hierzenberger, M. (2010). Empirical Secondary Data Analysis. In: Price Regulation and Risk. Lecture Notes in Economics and Mathematical Systems, vol 641. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12047-3_4

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