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Portfolio optimization for student t and skewed t returns

Author

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  • Wenbo Hu
  • Alec Kercheval

Abstract

It is well-established that equity returns are not Normally distributed, but what should the portfolio manager do about this, and is it worth the effort? It is now feasible to employ better multivariate distribution families that capture heavy tails and skewness in the data; we argue that among the best are the Student t and skewed t distributions. These can be efficiently fitted to data, and show a much better fit to real returns than the Normal distribution. By examining efficient frontiers computed using different distributional assumptions, we show, using for illustration five stocks chosen from the Dow index, that the choice of distribution has a significant effect on how much available return can be captured by an optimal portfolio on the efficient frontier.

Suggested Citation

  • Wenbo Hu & Alec Kercheval, 2010. "Portfolio optimization for student t and skewed t returns," Quantitative Finance, Taylor & Francis Journals, vol. 10(1), pages 91-105.
    • Handle: RePEc:taf:quantf:v:10:y:2010:i:1:p:91-105
    DOI: 10.1080/14697680902814225
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    References listed on IDEAS

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    1. Kjersti Aas & Ingrid Hobaek Haff, 2006. "The Generalized Hyperbolic Skew Student's t-Distribution," Journal of Financial Econometrics, Oxford University Press, vol. 4(2), pages 275-309.
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