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A general approach to Bayesian portfolio optimization

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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.

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Authors and Affiliations

  1. Graduate School of Risk Management, University of Cologne, Albertus-Magnus-Platz, 50923, Cologne, Germany

    Alexander Bade

  2. Department of Economic and Social Statistics, University of Cologne, Albertus-Magnus-Platz, 50923, Cologne, Germany

    Gabriel Frahm

  3. Department of Mathematics and Technology, University of Applied Sciences Koblenz, RheinAhrCampus, Südallee 2, 53424, Remagen, Germany

    Uwe Jaekel

Authors
  1. Alexander Bade
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  2. Gabriel Frahm
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  3. Uwe Jaekel
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Correspondence to Alexander Bade.

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Bade, A., Frahm, G. & Jaekel, U. A general approach to Bayesian portfolio optimization. Math Meth Oper Res 70, 337–356 (2009). https://doi.org/10.1007/s00186-008-0271-4

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  • DOI: https://doi.org/10.1007/s00186-008-0271-4

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