Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty
(2025) Journal of Financial and Quantitative Analysis — Vol. 60, n° 8, p. 3753-3790 (2025)
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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 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.
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Citations
Kan, R., & Lassance, N. (2025). Optimal Portfolio Choice with Fat Tails and Parameter Uncertainty. Journal of Financial and Quantitative Analysis, 60(8), 3753-3790. (Original work published 2025)