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

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Hierarchical Risk Parity : Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. / Lohre, Harald; Rother, Carsten; Schäfer, Kilian Axel.

Machine Learning for Asset Management: New Developments and Financial Applications. ed. / Emmanuel Jurczenko. Chichester : John Wiley & Sons, 2020. p. 332-368 (Innovation, Entrepreneurship and Management Series).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNChapter

Harvard

Lohre, H, Rother, C & Schäfer, KA 2020, Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. in E Jurczenko (ed.), Machine Learning for Asset Management: New Developments and Financial Applications. Innovation, Entrepreneurship and Management Series, John Wiley & Sons, Chichester, pp. 332-368. https://doi.org/10.1002/9781119751182.ch9

APA

Lohre, H., Rother, C., & Schäfer, K. A. (2020). Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. In E. Jurczenko (Ed.), Machine Learning for Asset Management: New Developments and Financial Applications (pp. 332-368). (Innovation, Entrepreneurship and Management Series). John Wiley & Sons. https://doi.org/10.1002/9781119751182.ch9

Vancouver

Lohre H, Rother C, Schäfer KA. Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. In Jurczenko E, editor, Machine Learning for Asset Management: New Developments and Financial Applications. Chichester: John Wiley & Sons. 2020. p. 332-368. (Innovation, Entrepreneurship and Management Series). https://doi.org/10.1002/9781119751182.ch9

Author

Lohre, Harald ; Rother, Carsten ; Schäfer, Kilian Axel. / Hierarchical Risk Parity : Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations. Machine Learning for Asset Management: New Developments and Financial Applications. editor / Emmanuel Jurczenko. Chichester : John Wiley & Sons, 2020. pp. 332-368 (Innovation, Entrepreneurship and Management Series).

Bibtex

@inbook{c54cccfcc3d74b06bea44e2eff23db92,
title = "Hierarchical Risk Parity: Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations",
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.",
author = "Harald Lohre and Carsten Rother and Sch{\"a}fer, {Kilian Axel}",
year = "2020",
month = jun,
day = "30",
doi = "10.1002/9781119751182.ch9",
language = "English",
isbn = "9781786305442",
series = "Innovation, Entrepreneurship and Management Series",
publisher = "John Wiley & Sons",
pages = "332--368",
editor = "Jurczenko, {Emmanuel }",
booktitle = "Machine Learning for Asset Management",

}

RIS

TY - CHAP

T1 - Hierarchical Risk Parity

T2 - Accounting for Tail Dependencies in Multi-asset Multi-factor Allocations

AU - Lohre, Harald

AU - Rother, Carsten

AU - Schäfer, Kilian Axel

PY - 2020/6/30

Y1 - 2020/6/30

N2 - 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.

AB - 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.

U2 - 10.1002/9781119751182.ch9

DO - 10.1002/9781119751182.ch9

M3 - Chapter

SN - 9781786305442

T3 - Innovation, Entrepreneurship and Management Series

SP - 332

EP - 368

BT - Machine Learning for Asset Management

A2 - Jurczenko, Emmanuel

PB - John Wiley & Sons

CY - Chichester

ER -