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.

References in corpus (2)

• Kullback-Leibler distance as a measure of the information filtered from multivariate data
• Cross-validation based Nonlinear Shrinkage