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Beta estimates of shares on the JSE Top 40 in the context of reference-day risk

Author

Listed:
  • Christopher Baker

    (University of Cape Town)

  • Kanshukan Rajaratnam

    (University of Cape Town)

  • Emlyn James Flint

    (University of Pretoria)

Abstract

A topic of interest in the finance world is measuring systematic risk. Accurately measuring the systematic risk component—or Beta—of an asset or portfolio is important in many financial applications. In this work, we consider the efficiency of a range of Beta estimation methods commonly used in practice from a reference-day risk perspective. We show that, when using the industry standard data sample of 5 years of monthly returns, the choice of reference-day used to calculate underlying returns has a significant impact on all of the Beta estimation methods considered. Driven by this finding, we propose and test an alternative nonparametric bootstrap approach for calculating Beta estimates which is unaffected by reference-day risk. Our primary goal is to determine a point-estimate of Beta, independent of reference-day.

Suggested Citation

  • Christopher Baker & Kanshukan Rajaratnam & Emlyn James Flint, 2016. "Beta estimates of shares on the JSE Top 40 in the context of reference-day risk," Environment Systems and Decisions, Springer, vol. 36(2), pages 126-141, June.
    • Handle: RePEc:spr:envsyd:v:36:y:2016:i:2:d:10.1007_s10669-016-9595-4
    DOI: 10.1007/s10669-016-9595-4
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    References listed on IDEAS

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    8. David Cademartori & Cecilia Romo & Ricardo Campos & Manuel Galea, 2003. "Robust estimation of systematic risk using the t distribution in the chilean stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 10(7), pages 447-453.
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    Cited by:

    1. Joe Hirschberg & Jenny Lye, 2021. "Estimating risk premiums for regulated firms when accounting for reference-day variation and high-order moments of return volatility," Environment Systems and Decisions, Springer, vol. 41(3), pages 455-467, September.
    2. Qifeng Qiao & Peter A. Beling, 2016. "Decision analytics and machine learning in economic and financial systems," Environment Systems and Decisions, Springer, vol. 36(2), pages 109-113, June.

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