Beyond Risk Parity - A Machine Learning-based Hierarchical Risk Parity Approach on Cryptocurrencies

14 Pages Posted: 11 Feb 2020

See all articles by Tobias Burggraf

Tobias Burggraf

WHU - Otto Beisheim School of Management

Aditya Vyas

affiliation not provided to SSRN

Date Written: February 8, 2020

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 covariance estimation windows and methodologies.

Keywords: Machine learning, Graph Theory, Hierarchical Tree Clustering, Asset Allocation, Cryptocurrencies

JEL Classification: G15, G41

Suggested Citation

Burggraf, Tobias and Vyas, Aditya, Beyond Risk Parity - A Machine Learning-based Hierarchical Risk Parity Approach on Cryptocurrencies (February 8, 2020). Available at SSRN: https://ssrn.com/abstract=3534773 or http://dx.doi.org/10.2139/ssrn.3534773

Tobias Burggraf (Contact Author)

WHU - Otto Beisheim School of Management ( email )

Burgplatz 2
Vallendar, 56179
Germany

Aditya Vyas

affiliation not provided to SSRN

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
62
Abstract Views
250
rank
374,676
PlumX Metrics