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RFS Advance Access published online on February 10, 2005
Review of Financial Studies, doi:10.1093/rfs/hhi014
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The Review of Financial Studies © The Author 2005. Published by Oxford University Press on behalf of the Society of for Financial Studies. All rights reserved. For Permissions, please email: journals.permissions@oupjournals.org

Original Articles

A Shrinkage Approach to Model Uncertainty and Asset Allocation

Zhenyu Wang 1*
1 University of Texas at Austin, McCombs School of Business

* To whom correspondence should be addressed.
Zhenyu Wang, E-mail: zw274{at}mail.utexas.edu


  Abstract

This paper takes a shrinkage approach to examine the empirical implications of aversion to model uncertainty. The shrinkage approach explicitly shows how predictive distributions incorporate data and prior beliefs. It enables us to solve the optimal portfolios for uncertainty-averse investors. Aversion to uncertainty about the CAPM leads investors to hold a portfolio that is not mean-variance efficient for any predictive distribution. However, mean-variance efficient portfolios corresponding to extremely strong beliefs in the Fama-French model are approximately optimal for uncertainty-averse investors. The empirical Bayes approach does not result in optimal portfolios for investors who are averse to model uncertainty.


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