# Deep Hedging: Learning to Simulate Equity Option Markets

arXiv: https://arxiv.org/abs/1911.01700 · HTML: https://mlsanity.com/paper/1911.01700/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Nov 2019.

## Built on by (most cited first, 25 of 25)

- [Sig-Wasserstein GANs for Time Series Generation](https://mlsanity.com/paper/2111.01207/) (2021)
- [Quantum Deep Hedging](https://mlsanity.com/paper/2303.16585/) (2023)
- [Multi-Asset Spot and Option Market Simulation](https://mlsanity.com/paper/2112.06823/) (2021)
- [Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measures](https://mlsanity.com/paper/2111.07844/) (2021)
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- [Simulating financial time series using attention](https://mlsanity.com/paper/2207.00493/) (2022)
- [Universal randomised signatures for generative time series modelling](https://mlsanity.com/paper/2406.10214/) (2024)
- [Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance](https://mlsanity.com/paper/2501.03993/) (2025)
- [Deep Learning for Systemic Risk Measures](https://mlsanity.com/paper/2207.00739/) (2022)
- [Generative Adversarial Network: Some Analytical Perspectives](https://mlsanity.com/paper/2104.12210/) (2021)
- [Convergence of GANs Training: A Game and Stochastic Control Methodology](https://mlsanity.com/paper/2112.00222/) (2021)
- [Estimating risks of option books using neural-SDE market models](https://mlsanity.com/paper/2202.07148/) (2022)
- [Risk-Neutral Market Simulation](https://mlsanity.com/paper/2202.13996/) (2022)
- [Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structures](https://mlsanity.com/paper/2306.15835/) (2023)
- [Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluation](https://mlsanity.com/paper/2502.17011/) (2025)
- [Quantifying Quality of Class-Conditional Generative Models in Time-Series Domain](https://mlsanity.com/paper/2210.07617/) (2022)
- [Deep Learning for Mean Field Games with non-separable Hamiltonians](https://mlsanity.com/paper/2301.02877/) (2023)
- [STEB: In Search of the Best Evaluation Approach for Synthetic Time Series](https://mlsanity.com/paper/2505.21160/) (2025)
- [Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsampling](https://mlsanity.com/paper/2506.07299/) (2025)
- [Generating Financial Time Series by Matching Random Convolutional Features](https://mlsanity.com/paper/2606.05138/) (2026)
- [DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfaces](https://mlsanity.com/paper/2608.12587/) (2026)
- [Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamics](https://mlsanity.com/paper/2103.11948/) (2021)
- [Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimization](https://mlsanity.com/paper/2407.14573/) (2024)

## Abstract (arXiv)

We construct realistic equity option market simulators based on generative adversarial networks (GANs). We consider recurrent and temporal convolutional architectures, and assess the impact of state compression. Option market simulators are highly relevant because they allow us to extend the limited real-world data sets available for the training and evaluation of option trading strategies. We show that network-based generators outperform classical methods on a range of benchmark metrics, and adversarial training achieves the best performance. Our work demonstrates for the first time that GANs can be successfully applied to the task of generating multivariate financial time series.