Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach
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.
Year of publication: |
2007
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Authors: | Garlappi, Lorenzo ; Uppal, Raman ; Wang, Tan |
Published in: |
Review of Financial Studies. - Society for Financial Studies - SFS. - Vol. 20.2007, 1, p. 41-81
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Publisher: |
Society for Financial Studies - SFS |
Saved in:
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