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

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Author Info
Lorenzo Garlappi
Raman Uppal
Tan Wang
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: (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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File URL: http://hdl.handle.net/10.1093/rfs/hhl003
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Article provided by Oxford University Press for 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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  1. Lutgens, Frank & Schotman, Peter C, 2007. "Robust Portfolio Optimisation with Multiple Experts," CEPR Discussion Papers 6161, C.E.P.R. Discussion Papers. [Downloadable!] (restricted)
  2. Thomas J. Brennan & Andrew W. Lo, 2008. "Impossible Frontiers," NBER Working Papers 14525, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
  3. Ju, Nengjiu & Miao, Jianjun, 2009. "Ambiguity, Learning, and Asset Returns," MPRA Paper 14737, University Library of Munich, Germany, revised Apr 2009. [Downloadable!]
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