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

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  • Raman Uppal

    (London Business School)

  • Lorenzo Garlappi

    (McCommbs School of Business, University of Texas)

  • Tan Wang

    (University of British Columbia)

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.

(This abstract was borrowed from another version of this item.)

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Bibliographic Info

Paper provided by Money Macro and Finance Research Group in its series Money Macro and Finance (MMF) Research Group Conference 2004 with number 54.

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Date of creation: 17 Sep 2004
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Handle: RePEc:mmf:mmfc04:54

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Web page: http://www.essex.ac.uk/afm/mmf/index.html

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  33. repec:cup:macdyn:v:6:y:2002:i:1:p:40-84 is not listed on IDEAS
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