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Portfolio Choice and Estimation Risk. A Comparison of Bayesian to Heuristic Approaches*

Published online by Cambridge University Press:  17 April 2015

Herold Ulf  and
Maurer Raimond
Herold Ulf
Affiliation:
Metzler Investment GmbH and Johann Wolfgang Goethe University, Frankfurt/Main, Germany, Grosse Gallusstr. 18, 60311 Frankfurt/Main, Germany, Tel.: +49 69 2104 1138, Email: UHerold@Metzler.com
Maurer Raimond
Affiliation:
Johann Wolfgang Goethe University, Frankfurt/Main, Germany Faculty of Business and Economics, Department of Finance, Senckenberganlage 31-33 (Uni-PF 58), 60054 Frankfurt/Main, Germany, Tel.: +49 69 798 25227, Email: RMaurer@wiwi.uni-frankfurt.de
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Abstract

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Estimation risk is known to have a huge impact on mean/variance optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. This issue has attracted new interest in the last years, and several approaches to incorporate estimation risk into portfolio selection have been developed only recently. In this article, we review these approaches as well as some older ones and compare them in an empirical out-of-sample study. The approaches can be classified along two criteria. First, we can differentiate heuristic approaches (restricting portfolio weights and employing simulation techniques) and those based on Bayesian statistics (shrinking the portfolios towards a pre-determined target). Second, the assumptions about the return-generating process differ, either assuming returns to be IID distributed or to be partly predictable. The central result of our empirical study is that all of the IID approaches, whether they account for estimation risk or not, are not superior to simple investment strategies like holding the market portfolio. A risk-adjusted outperformance is possible only if sample means are substituted with conditional expected return estimates. Furthermore, the Bayesian approaches reduce turnover and stabilize portfolio weights.

Keywords

Portfolio selectionestimation riskpredictive regressions
Type
Articles
Information
ASTIN Bulletin: The Journal of the IAA , Volume 36 , Issue 1 , May 2006 , pp. 135 - 160
Copyright
Copyright © ASTIN Bulletin 2006

Footnotes

*

The authors profited from the comments of two anonymous referees. The authors also thank Thanh H. Vo for excellent research assistance. This research project was supported by the German Investment and Asset Management Association and the E-finance lab at the Goethe-University Frankfurt.

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