Tail mean-variance portfolio selection with estimation risk

Zhenzhen Huang, Pengyu Wei and Chengguo Weng

Insurance: Mathematics and Economics, 2024, vol. 116, issue C, 218-234

Abstract: Tail Mean-Variance (TMV) has emerged from the actuarial community as a criterion for risk management and portfolio selection, with a focus on extreme losses. The existing literature on portfolio optimization under the TMV criterion relies on the plug-in approach that substitutes the unknown mean vector and covariance matrix of asset returns in the optimal portfolio weights with their sample counterparts. However, the plug-in method inevitably introduces estimation risk and usually leads to poor out-of-sample portfolio performance. To address this issue, we propose a combination of the plug-in and 1/N rules and optimize its expected out-of-sample performance. Our study is based on the Mean-Variance-Standard-deviation (MVS) performance measure, which encompasses the TMV, classical Mean-Variance, and Mean-Standard-Deviation (MStD) as special cases. The MStD criterion is particularly relevant to mean-risk portfolio selection when risk is measured by quantile-based risk measures. Our proposed combined portfolio consistently outperforms both the plug-in MVS and 1/N portfolios in simulated and real-world datasets.

Keywords: Tail mean-variance; Portfolio selection; Estimation risk; Portfolio combination; Out-of-sample performance (search for similar items in EconPapers)
JEL-codes: C13 D81 G11 G22 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:insuma:v:116:y:2024:i:c:p:218-234

DOI: 10.1016/j.insmatheco.2024.03.001

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