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Political and Regulatory Risk in Water Utilities: Beta Sensitivity in the United Kingdom

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  • Roger Buckland
  • Patricia Fraser

Abstract

UK utilities are generally regulated by the periodic setting of a price cap (the RPI‐X mechanism). To establish these caps, regulators must determine what returns are appropriate on the capital employed by utilities. This paper addresses the issue of the level of risk inherent in investment in the equity of regulated water utilities in the UK. It uses the techniques of the Kalman Filter to estimate daily betas for the major utilities in the period from privatisation to mid‐1999. The paper demonstrates that water utilities' risk is time‐variant. It demonstrates, also, that there have been significant political and regulatory influences in the systematic risk faced by water utility shareholders. It finds beta to display little evidence of cyclical variation across the regulatory review cycle. The paper also confirms that significant excess returns have been generated over the history of the privatised water sector and suggests that over‐estimation of systematic risk faced by investors in the sector may imply further excess returns in the next regulatory review period.

Suggested Citation

  • Roger Buckland & Patricia Fraser, 2001. "Political and Regulatory Risk in Water Utilities: Beta Sensitivity in the United Kingdom," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 28(7‐8), pages 877-904, September.
  • Handle: RePEc:bla:jbfnac:v:28:y:2001:i:7-8:p:877-904
    DOI: 10.1111/1468-5957.00397
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    Cited by:

    1. Roger Buckland & Julian Williams & Janice Beecher, 2015. "Risk and regulation in water utilities: a cross-country comparison of evidence from the CAPM," Journal of Regulatory Economics, Springer, vol. 47(2), pages 117-145, April.
    2. Xinwen Ni & Wolfgang Karl Hardle & Taojun Xie, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," Papers 2009.12121, arXiv.org, revised Aug 2021.
    3. World Bank, 2006. "Approaches to Private Participation in Water Services : A Toolkit," World Bank Publications - Books, The World Bank Group, number 6982, December.
    4. Fiocco, Raffaele & Guo, Dongyu, 2020. "Regulatory risk, vertical integration, and upstream investment," European Economic Review, Elsevier, vol. 128(C).
    5. Rajibur Reza & Gurudeo Anand Tularam & Xiyang Li & Bin Li, 2022. "Investments in the Asian water sector: an analysis based on the DCC-GARCH model," Palgrave Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
    6. Thomas A. Tsalis & Ioannis E. Nikolaou & Konstantinos P. Tsagarakis, 2020. "A benchmarking–scoring methodology for assessing the water risk disclosures of water utilities," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(7), pages 6473-6493, October.
    7. Barajas, Angel, 2004. "Modelo de valoración de clubes de fútbol basado en los factores clave de su negocio [Valuation model for football clubs based on the key factors of their business]," MPRA Paper 13158, University Library of Munich, Germany.
    8. Giulia Romano & Andrea Guerrini, 2019. "Paying Returns to Shareholders of Water Utilities: Evidence from Italy," Sustainability, MDPI, vol. 11(7), pages 1-12, April.
    9. Knieps, Günter & Weiß, Hans-Jörg, 2008. "Regulatory agencies and regulatory risk," Discussion Papers 118 [rev.], University of Freiburg, Institute for Transport Economics and Regional Policy.
    10. Knieps, Günter & Weiß, Hans-Jörg, 2007. "Reduction of regulatory risk: a network economic approach," Discussion Papers 117, University of Freiburg, Institute for Transport Economics and Regional Policy.
    11. Reza, Rajibur & Tularam, Gurudeo Anand & Li, Bin, 2021. "A review of global research on private investment in the water sector," Utilities Policy, Elsevier, vol. 72(C).
    12. Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".

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