Beyond risk parity – A machine learning-based hierarchical risk parity approach on cryptocurrencies

Abstract

It has long been known that estimating large empirical covariance matrices can lead to very unstable solutions, with estimation errors more than offsetting the benefits of diversification. In this study, we employ the Hierarchical Risk Parity approach, which applies state-of-the-art mathematics including graph theory and unsupervised machine learning to a large portfolio of cryptocurrencies. An out-of-sample comparison with traditional risk-minimization methods reveals that Hierarchical Risk Parity outperforms in terms of tail risk-adjusted return, thereby working as a potential risk management tool that can help cryptocurrency investors to better manage portfolio risk. The results are robust to different rebalancing intervals, covariance estimation windows and methodologies.