Portfolio choice and estimation risk : a comparison of Bayesian approaches to resampled efficiency

  • Estimation risk is known to have a huge impact on mean/variance (MV) optimized portfolios, which is one of the primary reasons to make standard Markowitz optimization unfeasible in practice. Several approaches to incorporate estimation risk into portfolio selection are suggested in the earlier literature. These papers regularly discuss heuristic approaches (e.g., placing restrictions on portfolio weights) and Bayesian estimators. Among the Bayesian class of estimators, we will focus in this paper on the Bayes/Stein estimator developed by Jorion (1985, 1986), which is probably the most popular estimator. We will show that optimal portfolios based on the Bayes/Stein estimator correspond to portfolios on the original mean-variance efficient frontier with a higher risk aversion. We quantify this increase in risk aversion. Furthermore, we review a relatively new approach introduced by Michaud (1998), resampling efficiency. Michaud argues that the limitations of MV efficiency in practice generally derive from a lack of statistical understanding of MV optimization. He advocates a statistical view of MV optimization that leads to new procedures that can reduce estimation risk. Resampling efficiency has been contrasted to standard Markowitz portfolios until now, but not to other approaches which explicitly incorporate estimation risk. This paper attempts to fill this gap. Optimal portfolios based on the Bayes/Stein estimator and resampling efficiency are compared in an empirical out-of-sample study in terms of their Sharpe ratio and in terms of stochastic dominance.

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Author:Ulf HeroldGND, Raimond MaurerORCiDGND
Parent Title (English):Universität Frankfurt am Main. Fachbereich Wirtschaftswissenschaften: [Working paper series / Finance and accounting] Working paper series, Finance & Accounting ; No. 94
Series (Serial Number):Working paper series / Johann-Wolfgang-Goethe-Universität Frankfurt am Main, Fachbereich Wirtschaftswissenschaften : Finance & Accounting (94)
Publisher:Univ., Fachbereich Wirtschaftswiss.
Place of publication:Frankfurt am Main
Document Type:Working Paper
Year of Completion:2002
Year of first Publication:2002
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2005/10/07
GND Keyword:Portfolio Selection; Risiko; Bayes-Verfahren
Page Number:43
Institutes:Wirtschaftswissenschaften / Wirtschaftswissenschaften
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
JEL-Classification:C Mathematical and Quantitative Methods / C1 Econometric and Statistical Methods: General / C11 Bayesian Analysis
Licence (German):License LogoDeutsches Urheberrecht