# Deep Hedging, Generative Adversarial Networks, and Beyond

arXiv: https://arxiv.org/abs/2103.03913 · HTML: https://mlsanity.com/paper/2103.03913/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [Improved Data Generation for Enhanced Asset Allocation: A Synthetic Dataset Approach for the Fixed Income Universe](https://mlsanity.com/paper/2311.16004/) (2023)

## Abstract (arXiv)

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.