Mean-Semivariance Portfolio Optimization Using Minimum Average Partial

23 Pages Posted: 18 Mar 2020 Last revised: 12 Oct 2021

See all articles by Andrea Rigamonti

Andrea Rigamonti

University of Liechtenstein

Katarina Lucivjanska

University of Pavol Jozef Šafárik in Kosice

Date Written: October 11, 2021

Abstract

Mean-semivariance and minimum semivariance portfolios are a preferable alternative to mean-variance and minimum variance portfolios whenever the asset returns are not symmetrically distributed. However, similarly to other portfolios based on downside risk measures, they typically perform poorly in practice because the estimates of the necessary inputs are less reliable than the estimates of the full covariance matrix. We address this problem by performing PCA using the Minimum Average Partial on the downside correlation matrix in order to reduce the dimension of the problem and, with it, the estimation errors. We apply our strategy to several datasets and show that it greatly improves the performance of mean-semivariance optimization, largely closing the gap in out-of-sample performance with the strategies based on the covariance matrix.

Keywords: semivariance, principal component analysis, minimum average partial, parameter uncertainty, portfolio optimization

JEL Classification: C38, G11

Suggested Citation

Rigamonti, Andrea and Lucivjanska, Katarina, Mean-Semivariance Portfolio Optimization Using Minimum Average Partial (October 11, 2021). Available at SSRN: https://ssrn.com/abstract=3542727 or http://dx.doi.org/10.2139/ssrn.3542727

Andrea Rigamonti (Contact Author)

University of Liechtenstein ( email )

Fuerst Franz Josef-Strasse
Vaduz, 9490
Liechtenstein

Katarina Lucivjanska

University of Pavol Jozef Šafárik in Kosice ( email )

Šrobárova 2
Košice, 041 32
Slovakia

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