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Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty

Published online by Cambridge University Press:  11 August 2025

Raymond Kan  and
Nathan Lassance Open the ORCID record for Nathan Lassance [Opens in a new window]
Raymond Kan
Affiliation:
https://ror.org/03dbr7087 University of Toronto Rotman School of Management raymond.kan@rotman.utoronto.ca
Nathan Lassance*
Affiliation:
https://ror.org/02495e989 UCLouvainLFIN/LIDAM
*
nathan.lassance@uclouvain.be (corresponding author)
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Abstract

Existing portfolio combination rules that optimize the out-of-sample performance under parameter uncertainty assume multivariate normally distributed returns. However, we show that this assumption is not innocuous because fat tails in returns lead to poorer out-of-sample performance of the sample mean–variance and sample global minimum-variance (GMV) portfolios relative to normality. Consequently, when returns are fat-tailed, portfolio combination rules should allocate less to the sample mean–variance and sample GMV portfolios, and more to the risk-free asset, than the normality assumption prescribes. Empirical evidence shows that accounting for fat tails in the construction of optimal portfolio combination rules significantly improves their out-of-sample performance.

Information

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , First View , pp. 1 - 38
Copyright
© The Author(s), 2025. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We are grateful to George G. Pennacchi (the editor), Konark Saxena (the referee), Xinghua Zheng, and Guofu Zhou for their valuable comments.

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