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

arXiv: https://arxiv.org/abs/2005.08703 · HTML: https://mlsanity.com/paper/2005.08703/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 May 2020.

## Built on by (most cited first, 1 of 1)

- [End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning](https://mlsanity.com/paper/2507.01918/) (2025)

## Abstract (arXiv)

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.