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

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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.

Dates and versions

hal-02612262 , version 1 (19-05-2020)

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