Tail-Risk Protection: Machine Learning Meets Modern Econometrics

32 Pages Posted: 2 Dec 2020 Last revised: 24 Aug 2021

See all articles by Bruno Spilak

Bruno Spilak

Humboldt-Universität zu Berlin

Wolfgang K. Härdle

Blockchain Research Center; Xiamen University - Wang Yanan Institute for Studies in Economics (WISE); Charles University; National Yang Ming Chiao Tung University; Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Date Written: October 7, 2020

Abstract

Tail risk protection is in the focus of the financial industry and requires solid mathematical and statistical tools, especially when a trading strategy is derived. Recent hype driven by machine learning (ML) mechanisms has raised the necessity to display and understand the functionality of ML tools. In this paper, we present a dynamic tail risk protection strategy that targets a maximum predefined level of risk measured by Value-At-Risk while controlling for participation in bull market regimes. We propose different weak classifiers, parametric and non-parametric, that estimate the exceedance probability of the risk level from which we derive trading signals in order to hedge tail events. We then compare the different approaches both with statistical and trading strategy performance, finally we propose an ensemble classifier that produces a meta tail risk protection strategy improving both generalization and trading performance.

Suggested Citation

Spilak, Bruno and Härdle, Wolfgang K., Tail-Risk Protection: Machine Learning Meets Modern Econometrics (October 7, 2020). Available at SSRN: https://ssrn.com/abstract=3714632 or http://dx.doi.org/10.2139/ssrn.3714632

Bruno Spilak (Contact Author)

Humboldt-Universität zu Berlin ( email )

Humboldt Universität
Unter den Linden 6
Berlin, 10099
Germany

Wolfgang K. Härdle

Blockchain Research Center ( email )

Unter den Linden 6
Berlin, D-10099
Germany

Xiamen University - Wang Yanan Institute for Studies in Economics (WISE) ( email )

A 307, Economics Building
Xiamen, Fujian 10246
China

Charles University ( email )

Celetná 13
Dept Math Physics
Praha 1, 116 36
Czech Republic

National Yang Ming Chiao Tung University ( email )

No. 1001, Daxue Rd. East Dist.
Hsinchu City 300093
Taiwan

Humboldt University of Berlin - Center for Applied Statistics and Economics (CASE)

Unter den Linden 6
Berlin, D-10099
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
205
Abstract Views
765
rank
210,035
PlumX Metrics