Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty

Raymond Kan () and Nathan Lassance ()
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Raymond Kan: Rotman School of Management, University of Toronto
Nathan Lassance: Université catholique de Louvain, LIDAM/LFIN, Belgium

No 2024011, LIDAM Reprints LFIN from Université catholique de Louvain, Louvain Finance (LFIN)

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 portfolios relative to normality. Consequently, when returns are fat-tailed, portfolio combination rules should allocate less to the sample mean-variance and sample global minimum-variance 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.

Keywords: Portfolio combination; elliptical distribution; estimation risk (search for similar items in EconPapers)
Pages: 88
Date: 2024-11-25
Note: In: Journal of Financial and Quantitative Analysis, 2025
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Persistent link: https://EconPapers.repec.org/RePEc:ajf:louvlr:2024011

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