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RFS Advance Access published online on May 15, 2006
Review of Financial Studies, doi:10.1093/rfs/hhl003
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© The Author 2006. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please email: journals.permissions@oxfordjournals.org

Article

Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach

Lorenzo Garlappi 1 *, Raman Uppal 2, and Tan Wang 3
1 McCombs School of Business, The University of Texas at Austin, Austin TX, 78712
2 London Business School and CEPR; IFA, 6 Sussex Place Regent’s Park, London, United Kingdom NW1 4SA
3 Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver Canada V6T 1Z2, Canada

* To whom correspondence should be addressed.
Lorenzo Garlappi, E-mail: lorenzo.garlappi{at}mccombs.utexas.edu


  Abstract

We develop a model for an investor with multiple priors and aversion to ambiguity. We characterize the multiple priors by a "confidence interval" around the estimated expected returns and we model ambiguity aversion via a minimization over the priors. Our model has several attractive features: One, it has a solid axiomatic foundation. Two, it is flexible enough to allow for different degrees of uncertainty about expected returns for various subsets of assets and also about the return-generating model. Three, it delivers closed-form expressions for the optimal portfolio. Our empirical analysis suggests that, compared to portfolios from classical and Bayesian models, ambiguity-averse portfolios are more stable over time and deliver a higher out-of sample Sharpe ratio.

Keywords: Portfolio choice, asset allocation, estimation error, ambiguity, uncertainty, robustness.
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