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

Christian Bongiorno () and Damien Challet
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Christian Bongiorno: MICS - Mathématiques et Informatique pour la Complexité et les Systèmes - CentraleSupélec - Université Paris-Saclay

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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.

Date: 2021-08-19
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Published in European Journal of Finance, 2021, 28 (13-15), pp.1344-1360. ⟨10.1080/1351847X.2021.1963301⟩

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Working Paper: Reactive Global Minimum Variance Portfolios with $k-$BAHC covariance cleaning (2023)
Journal Article: Reactive global minimum variance portfolios with k-BAHC covariance cleaning (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-02612262

DOI: 10.1080/1351847X.2021.1963301

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