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

See all articles by Harald Lohre

Harald Lohre

Robeco Quantitative Investments; Lancaster University Management School

Carsten Rother

Invesco; University of Hamburg

Kilian Axel Schäfer

Metzler Asset Management

Date Written: January 23, 2020

Abstract

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

Lohre, Harald and Rother, Carsten and Schäfer, Kilian Axel, Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations (January 23, 2020). Chapter 9 in: Machine Learning and Asset Management, Emmanuel Jurczenko (ed.), Iste and Wiley, 2020, pp. 332-368, Available at SSRN: https://ssrn.com/abstract=3513399 or http://dx.doi.org/10.2139/ssrn.3513399

Harald Lohre (Contact Author)

Robeco Quantitative Investments ( email )

Weena 850
Rotterdam, 3011 AG
Netherlands

Lancaster University Management School

Bailrigg
Lancaster LA1 4YX
United Kingdom

HOME PAGE: http://www.lancaster.ac.uk/lums/people/harald-lohre

Carsten Rother

Invesco ( email )

An der Welle 5
Frankfurt am Main, 60322
Germany

University of Hamburg ( email )

Allende-Platz 1
Hamburg, 20146
Germany

Kilian Axel Schäfer

Metzler Asset Management ( email )

Do you have negative results from your research you’d like to share?

Paper statistics

Downloads
3,577
Abstract Views
10,393
Rank
5,911
PlumX Metrics