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A general approach to Bayesian portfolio optimization

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  • Alexander Bade
  • Gabriel Frahm
  • Uwe Jaekel

Abstract

We develop a general approach to portfolio optimization taking account of estimation risk and stylized facts of empirical finance. This is done within a Bayesian framework. The approximation of the posterior distribution of the unknown model parameters is based on a parallel tempering algorithm. The portfolio optimization is done using the first two moments of the predictive discrete asset return distribution. For illustration purposes we apply our method to empirical stock market data where daily asset log-returns are assumed to follow an orthogonal MGARCH process with t-distributed perturbations. Our results are compared with other portfolios suggested by popular optimization strategies. Copyright Springer-Verlag 2009

Suggested Citation

  • Alexander Bade & Gabriel Frahm & Uwe Jaekel, 2009. "A general approach to Bayesian portfolio optimization," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 70(2), pages 337-356, October.
    • Handle: RePEc:spr:mathme:v:70:y:2009:i:2:p:337-356
    DOI: 10.1007/s00186-008-0271-4
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    3. Dimitrios Bisias & Andrew W Lo & James F Watkins, 2012. "Estimating the NIH Efficient Frontier," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-10, May.

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