Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets

This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using cellwise robust association measures, the minCluster portfolio is able to retrieve the underlying hier...

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Bibliographic Details
Published inThe Journal of finance and data science Vol. 9; p. 100097
Main Authors Menvouta, Emmanuel Jordy, Serneels, Sven, Verdonck, Tim
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.11.2023
KeAi Communications Co., Ltd
Subjects
Cellwise robustness
Distance correlation
Hierarchical clustering
Linkage
Portfolio allocation
Risk contribution
Distance correlation
Portfolio allocation
Linkage
Risk contribution
Hierarchical clustering
Cellwise robustness
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Summary:This paper introduces the minCluster portfolio, which is a portfolio optimization method combining the optimization of downside risk measures, hierarchical clustering and cellwise robustness. Using cellwise robust association measures, the minCluster portfolio is able to retrieve the underlying hierarchical structure in the data. Furthermore, it provides downside protection by using tail risk measures for portfolio optimization. We show through simulation studies and a real data example that the minCluster portfolio produces better out-of-sample results than mean-variances or other hierarchical clustering based approaches. Cellwise outlier robustness makes the minCluster method particularly suitable for stable optimization of portfolios in highly volatile markets, such as portfolios containing cryptocurrencies.
ISSN:2405-9188
2405-9188
DOI:10.1016/j.jfds.2023.100097