Robust deep hedging

TL;DR

This paper introduces a deep hedging method for robust pricing and hedging under parameter uncertainty in generalized affine processes, demonstrating improved performance over existing methods especially during volatile periods.

Contribution

It proposes a novel deep hedging approach that efficiently addresses parameter uncertainty in a broad class of Markov processes, including Black-Scholes and CEV models.

Findings

Robust deep hedging outperforms existing methods in volatile markets.

The approach enables fast numerical pricing within a variational Kolmogorov framework.

Numerical evaluations on real and simulated data validate the method's effectiveness.

Abstract

We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black-Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches, in particular in highly volatile periods.