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...
Saved in:
Published in | The Journal of finance and data science Vol. 9; p. 100097 |
---|---|
Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Elsevier B.V
01.11.2023
KeAi Communications Co., Ltd |
Subjects | |
Online Access | Get full text |
Cover
Loading…
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 |