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

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.
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Contributor : Christian Bongiorno Connect in order to contact the contributor
Submitted on : Tuesday, May 19, 2020 - 7:48:35 AM
Last modification on : Tuesday, December 14, 2021 - 3:01:56 AM

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  • HAL Id : hal-02612262, version 1
  • ARXIV : 2005.08703


Christian Bongiorno, Damien Challet. Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning. 2020. ⟨hal-02612262⟩



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