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Tail-risk protection: Machine Learning meets modern Econometrics

Bruno Spilak and Wolfgang Härdle ()

No 2020-015, IRTG 1792 Discussion Papers from Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"

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.

JEL-codes: C00 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-cmp, nep-cwa, nep-fmk, nep-ore and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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https://www.econstor.eu/bitstream/10419/230821/1/irtg1792dp2020-015.pdf (application/pdf)

Related works:
Chapter: Tail-Risk Protection: Machine Learning Meets Modern Econometrics (2022)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:irtgdp:2020015

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