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Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market

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  • David C. Bowie
  • David J. Bradfield

Abstract

In this paper we demonstrate that robust estimators improve the reliability of estimates of beta coefficients on small, thinly traded stock markets. We outline several different types of robust and bounded influence regression estimators and assess them using a jackknife methodology on data from the Johannesburg Stock Exchange. The empirical evidence confirms the hypothesis that robust estimators are more efficient than least squares estimators and indicates that least squares estimators may over‐estimate systematic risk in some cases.

Suggested Citation

  • David C. Bowie & David J. Bradfield, 1998. "Robust Estimation of Beta Coefficients: Evidence from a Small Stock Market," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 25(3‐4), pages 439-454, April.
    • Handle: RePEc:bla:jbfnac:v:25:y:1998:i:3-4:p:439-454
    DOI: 10.1111/1468-5957.00196
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    Cited by:

    1. Juan Carlos Gutierrez Betancur, 2017. "Robust Estimation of beta and the hedging ratio in Stock Index Futures In the Integrated Latin American Market," Revista Ecos de Economía, Universidad EAFIT, vol. 21(44), pages 37-71, June.
    2. Edna Schechtman & Shlomo Yitzhaki & Taina Pudalov, 2011. "Gini’s multiple regressions: two approaches and their interaction," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(1), pages 67-99.
    3. Cable, John & Holland, Kevin, 1999. "Regression vs. non-regression models of normal returns: implications for event studies," Economics Letters, Elsevier, vol. 64(1), pages 81-85, July.

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