Tail-Risk Protection: Machine Learning Meets Modern Econometrics

Bruno Spilak () and Wolfgang Härdle
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Bruno Spilak: Humboldt-Universität zu Berlin

Chapter 92 in Encyclopedia of Finance, 2022, pp 2177-2211 from Springer

Abstract: 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 chapter, 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 nonparametric, 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.

Keywords: Tail-risk; Trading strategy; Cryptocurrency; Value-At-Risk; Deep learning; Machine learning; Econometrics; Extreme value theory; Exceedance probability (search for similar items in EconPapers)
Date: 2022
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Working Paper: Tail-risk protection: Machine Learning meets modern Econometrics (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-91231-4_94

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DOI: 10.1007/978-3-030-91231-4_94

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