Do we need higher-order comoments to enhance mean-variance portfolios? Evidence from a simplified jump process

Khaldoun Khashanah, Majeed Simaan and Yusif Simaan

International Review of Financial Analysis, 2022, vol. 81, issue C

Abstract: We consider a joint distribution that decomposes asset returns into two independent components: an elliptical innovation (Gaussian) and a systematic non-elliptical latent process. The paper provides a tractable approach to estimate the underlying parameters and, hence, the assets’ exposures to the latent non-elliptical factor. Additionally, the framework incorporates higher-order moments, such as skewness and kurtosis, for portfolio selection. Taking into account estimation risk, we investigate the economic contribution of the non-elliptical term. Overall, we find weak empirical evidence to support the inclusion of the non-elliptical term and, hence, the higher-order comoments. Nonetheless, our findings support the mean–variance (MV) decision rule that incorporates the elliptical term alone. Excluding the non-elliptical term results in more robust mean–variance estimates and, thus, enhanced out-of-sample performance. This evidence is significant among stocks that exhibit a strong deviation from the Gaussian property. Moreover, it is most pronounced during market turmoils, when exposures to the latent factor are highest.

Keywords: Utility theory; Shrinkage; Multivariate analysis; Non-elliptical distributions (search for similar items in EconPapers)
JEL-codes: C13 C44 C46 G11 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:81:y:2022:i:c:s1057521922000412

DOI: 10.1016/j.irfa.2022.102068

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