Deep Hedging, Generative Adversarial Networks, and Beyond

TL;DR

This paper presents a deep reinforcement learning framework using RNN-based agents and GAN-generated data for improved delta hedging of options, outperforming traditional models in risk management and profit optimization.

Contribution

It introduces a novel RL-based hedging approach that leverages GAN-generated data, demonstrating superior performance over classic models in risk and profit metrics.

Findings

RL agents outperform Black-Scholes in tail risk minimization

GAN-generated paths enable better hedging performance

Framework offers flexible, AI-driven hedging solutions

Abstract

This paper introduces a potential application of deep learning and artificial intelligence in finance, particularly its application in hedging. The major goal encompasses two objectives. First, we present a framework of a direct policy search reinforcement agent replicating a simple vanilla European call option and use the agent for the model-free delta hedging. Through the first part of this paper, we demonstrate how the RNN-based direct policy search RL agents can perform delta hedging better than the classic Black-Scholes model in Q-world based on parametrically generated underlying scenarios, particularly minimizing tail exposures at higher values of the risk aversion parameter. In the second part of this paper, with the non-parametric paths generated by time-series GANs from multi-variate temporal space, we illustrate its delta hedging performance on various values of the risk aversion parameter via the basic RNN-based RL agent introduced in the first part of the paper, showing that we can potentially achieve higher average profits with a rather evident risk-return trade-off. We believe that this RL-based hedging framework is a more efficient way of performing hedging in practice, addressing some of the inherent issues with the classic models, providing promising/intuitive hedging results, and rendering a flexible framework that can be easily paired with other AI-based models for many other purposes.