Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations
Chapter 9 in: Machine Learning and Asset Management, Emmanuel Jurczenko (ed.), Iste and Wiley, 2020, pp. 332-368
33 Pages Posted: 8 Jan 2020 Last revised: 9 Nov 2020
Date Written: January 23, 2020
We investigate portfolio diversification strategies based on hierarchical clustering. These hierarchical risk parity strategies use graph theory and unsupervised machine learning to build diversified portfolios by acknowledging the hierarchical structure of the investment universe. In this chapter, we consider two dissimilarity measures for clustering a multi-asset multi-factor universe. While the Pearson correlation coefficient is a popular choice, we are especially interested in a measure based on the lower tail dependence coefficient. Such innovation is expected to achieve better tail risk management in the context of allocating to skewed style factor strategies. Indeed, the corresponding hierarchical risk parity strategies seem to have been navigating the associated downside risk better, yet come at the cost of high turnover. A comparison based on block-bootstrapping evidences alternative risk parity strategies along economic factors to be on par in terms of downside risk with those based on statistical clusters.
Keywords: Multi-asset Multi-factor Investing, Diversification, Hierarchical Risk Parity, Tail Dependence
JEL Classification: G11, D81
Suggested Citation: Suggested Citation