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Cited by 686

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Garlappi, Lorenzo, Uppal, Raman, Wang, Tan·2007·Review of Financial Studies

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)

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Metadata

Authors
Garlappi, L., Uppal, R., & Wang, T.
Venue
Review of Financial Studies
Published
2006-05-15
Volume
20
Issue
1
Pages
41-81
ISSN
  • 0893-9454 (ISSN-L)
  • 1465-7368
OpenAlex
W3124539603
Cited by
686
References
3
FWCI
37.41
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach. https://app.cochat.ai/papers/10.1093/rfs/hhl003
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Garlappi, L., Uppal, R., & Wang, T. (2007). Portfolio Selection with Parameter and Model Uncertainty: A Multi-Prior Approach. Review of Financial Studies. https://doi.org/10.1093/rfs/hhl003

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.