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

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Publication date30/06/2020
Host publicationMachine Learning for Asset Management: New Developments and Financial Applications
EditorsEmmanuel Jurczenko
Place of PublicationChichester
PublisherJohn Wiley & Sons
Number of pages37
ISBN (electronic)9781119751175
ISBN (print)9781786305442
<mark>Original language</mark>English

Publication series

NameInnovation, Entrepreneurship and Management Series


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.