Hierarchical Risk Parity:Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations

Lohre, Harald and Rother, Carsten and Schäfer, Kilian Axel (2020) Hierarchical Risk Parity:Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. In: Machine Learning for Asset Management. Innovation, Entrepreneurship and Management Series . John Wiley & Sons, Chichester, pp. 332-368. ISBN 9781786305442

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Abstract

This chapter examines the use and merits of hierarchical clustering techniques in the context of multi-asset multi-factor investing. In particular, it contrasts these techniques with several competing risk-based allocation paradigms, such as 1/N, minimum-variance, standard risk parity and diversified risk parity. The chapter introduces hierarchical risk parity (HRP) strategies based on the Pearson correlation coefficient and also introduces hierarchical clustering based on the lower tail dependence coefficient. The chapter provides an overview of traditional risk-based allocation strategies and outlines a framework to measure and manage portfolio diversification. It examines the performance of the introduced HRP strategies relative to the traditional alternatives. The chapter discusses Meucci's approach to managing diversification, which serves to construct a diversified risk parity strategy based on economic factors.

Item Type:
Contribution in Book/Report/Proceedings
ID Code:
161077
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Deposited On:
04 Nov 2021 17:25
Refereed?:
No
Published?:
Published
Last Modified:
19 Nov 2021 16:26