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Analysing the return distributions of Australian stocks: the CAPM, factor models and quantile regressions


  • David E. Allen
  • Abhay Kumar Singh
  • Robert Powell


Traditionally, ordinary least square (OLS) regression methods are used to test asset pricing models. This study focuses on the use of quantile regression as an alternative approach to the analysis of risk and return distributions in quantitative finance. It empirically examines the behaviour of two widely used asset pricing factors, beta and book to market ratios, but the focus is on minimising absolute deviations around the median rather than minimising squared deviations around the mean of their distributions, as we apply quantile regressions as opposed to OLS. We show how OLS is less able to capture the extreme values or the adverse losses in the return distribution, which on the other hand are captured by quantile regressions. The study not only shows that the factors do not necessarily follow a linear relationship but also shows that the traditional use of OLS becomes less effective when it comes to analysing the extremes within a distribution, which are often a source of keen interest for investors and risk managers.

Suggested Citation

  • David E. Allen & Abhay Kumar Singh & Robert Powell, 2013. "Analysing the return distributions of Australian stocks: the CAPM, factor models and quantile regressions," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 15(1), pages 88-109.
  • Handle: RePEc:ids:gbusec:v:15:y:2013:i:1:p:88-109

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    References listed on IDEAS

    1. Kenneth R. French, 2008. "Presidential Address: The Cost of Active Investing," Journal of Finance, American Finance Association, vol. 63(4), pages 1537-1573, August.
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    3. Giulio Palomba, 2008. "Multivariate GARCH models and the Black-Litterman approach for tracking error constrained portfolios: an empirical analysis," Global Business and Economics Review, Inderscience Enterprises Ltd, vol. 10(4), pages 379-413.
    4. Admati, Anat R & Pfleiderer, Paul, 1997. "Does It All Add Up? Benchmarks and the Compensation of Active Portfolio Managers," The Journal of Business, University of Chicago Press, vol. 70(3), pages 323-350, July.
    5. Nadima El-Hassan & Paul Kofman, 2003. "Tracking Error and Active Portfolio Management," Australian Journal of Management, Australian School of Business, vol. 28(2), pages 183-207, September.
    6. Steve Satchell & Soosung Hwang, 2001. "Tracking Error: Ex-Ante versus Ex-Post Measures," Working Papers wp01-15, Warwick Business School, Finance Group.
    7. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
    8. Rudolf, Markus & Wolter, Hans-Jurgen & Zimmermann, Heinz, 1999. "A linear model for tracking error minimization," Journal of Banking & Finance, Elsevier, vol. 23(1), pages 85-103, January.
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    Cited by:

    1. Chowdhury, Biplob & Jeyasreedharan, Nagaratnam & Dungey, Mardi, 2017. "Quantile relationships between standard, diffusion and jump betas across Japanese banks," Working Papers 2017-10, University of Tasmania, Tasmanian School of Business and Economics.
    2. repec:eee:intfin:v:50:y:2017:i:c:p:52-68 is not listed on IDEAS
    3. repec:bla:manchs:v:85:y:2017:i:2:p:212-242 is not listed on IDEAS


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