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

  • Lorenzo Garlappi
  • Raman Uppal
  • Tan Wang

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) Copyright 2007, Oxford University Press.

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Article provided by Society for Financial Studies in its journal The Review of Financial Studies.

Volume (Year): 20 (2007)
Issue (Month): 1 (January)
Pages: 41-81

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Handle: RePEc:oup:rfinst:v:20:y:2007:i:1:p:41-81
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