Publication
Title
Portfolio optimization using cellwise robust association measures and clustering methods with application to highly volatile markets
Author
Abstract
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.
Language
English
Source (journal)
The Journal of Finance and Data Science
Publication
2023
ISSN
24059188
2405-9188
DOI
10.1016/J.JFDS.2023.100097
Volume/pages
9 (2023) , p. 1-13
Article Reference
100097
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Publication type
Subject
Affiliation
Publications with a UAntwerp address
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Record
Identifier
Creation 05.12.2023
Last edited 06.12.2023
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