Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230821
Authors: 
Spilak, Bruno
Härdle, Wolfgang Karl
Year of Publication: 
2020
Series/Report no.: 
IRTG 1792 Discussion Paper No. 2020-015
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: 
C00
Document Type: 
Working Paper

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