Humboldt-Universität zu Berlin - High Dimensional Nonstationary Time Series

IRTG1792DP2020 015

Tail-risk protection: Machine Learning meets modern Econometrics

Bruno Spilak
Wolfgang Karl Härdle

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


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