Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning

Christian Bongiorno and Damien Challet

Papers from arXiv.org

Abstract: We introduce a $k$-fold boosted version of our Boostrapped Average Hierarchical Clustering cleaning procedure for correlation and covariance matrices. We then apply this method to global minimum variance portfolios for various values of $k$ and compare their performance with other state-of-the-art methods. Generally, we find that our method yields better Sharpe ratios after transaction costs than competing filtering methods, despite requiring a larger turnover.

Date: 2020-05, Revised 2023-03
New Economics Papers: this item is included in nep-ecm
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Journal Article: Reactive global minimum variance portfolios with k-BAHC covariance cleaning (2022)
Working Paper: Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning (2021)
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