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RFS Advance Access originally published online on May 15, 2006
Review of Financial Studies 2007 20(1):41-81; 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.

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

Lorenzo Garlappi
University of Texas at Austin

Raman Uppal
London Business School and CEPR

Tan Wang
University of British Columbia and CCFR

Address correspondence to Lorenzo Garlappi, McCombs School of Business, University of Texas at Austin, Austin, TX 78712, or e-mail: lorenzo.garlappi{at}mccombs.utexas.edu.

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: (1) it has a solid axiomatic foundation; (2) 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; and (3) it delivers closed-form expressions for the optimal portfolio. Our empirical analysis suggests that, compared with portfolios from classical and Bayesian models, ambiguity-averse portfolios are more stable over time and deliver a higher out-of sample Sharpe ratio. (JEL G11)


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