Tail-risk protection: Machine Learning meets modern Econometrics

TL;DR

This paper develops a dynamic tail risk protection strategy using machine learning classifiers to estimate exceedance probabilities, aiming to control risk levels while maintaining market participation, and introduces an ensemble approach for improved performance.

Contribution

It introduces a novel ML-based tail risk hedging method that combines parametric and non-parametric classifiers with ensemble techniques for enhanced risk management.

Findings

Ensemble classifier improves risk control and trading performance.

ML classifiers effectively estimate tail risk exceedance probabilities.

The proposed strategy maintains market participation while managing tail risk.

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.