Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-Asset Multi-Factor Allocations

Machine Learning and Asset Management, Forthcoming

33 Pages Posted: 8 Jan 2020

See all articles by Harald Lohre

Harald Lohre

Invesco; Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy, 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). Machine Learning and Asset Management, Forthcoming. Available at SSRN: https://ssrn.com/abstract=3513399 or http://dx.doi.org/10.2139/ssrn.3513399

Centre for Financial Econometrics, Asset Markets and Macroeconomic Policy, Lancaster University Management School

Bailrigg
Lancaster LA1 4YX
United Kingdom

HOME PAGE: http://www.lancaster.ac.uk/lums/research/research-centres/financial-econometrics/

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 )

Register to save articles to
your library

Register

Paper statistics

Downloads
161
Abstract Views
512
PlumX Metrics