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

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Listed:
  • 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.)

Suggested Citation

  • Raman Uppal & Lorenzo Garlappi & Tan Wang, 2004. "Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach," Money Macro and Finance (MMF) Research Group Conference 2004 54, Money Macro and Finance Research Group.
  • Handle: RePEc:mmf:mmfc04:54
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    References listed on IDEAS

    as
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    More about this item

    JEL classification:

    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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