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Portfolio optimization for student t and skewed t returns


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  • Wenbo Hu
  • Alec Kercheval
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    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.

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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Quantitative Finance.

    Volume (Year): 10 (2010)
    Issue (Month): 1 ()
    Pages: 91-105

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    Handle: RePEc:taf:quantf:v:10:y:2010:i:1:p:91-105

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    Keywords: Portfolio optimization; Portfolio management; Correlation modelling; Risk management; Value at risk; Model estimation;


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    Cited by:
    1. Daniel T. Cassidy & Michael J. Hamp & Rachid Ouyed, 2010. "Student's t-Distribution Based Option Sensitivities: Greeks for the Gosset Formulae," Papers 1003.1344,, revised Jul 2010.
    2. G├╝rtler, Marc & Rauh, Ronald, 2012. "Challenging traditional risk models by a non-stationary approach with nonparametric heteroscedasticity," Working Papers IF41V1, Technische Universit├Ąt Braunschweig, Institute of Finance.


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