mlsanityData to 7 Oct 2026

Deep Hedging: Learning to Simulate Equity Option Markets

arXiv 1911.01700 ↗First posted 5 Nov 2019q-fin.CP cs.LG q-fin.MFcited by 25

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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.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 6; 2023: 4; 2024: 2; 2025: 4; 2026: 2.

25 papers.

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  1. 2021Sig-Wasserstein GANs for Time Series Generationcited by 60
  2. 2023Quantum Deep Hedgingcited by 22
  3. 2021Multi-Asset Spot and Option Market Simulationcited by 17
  4. 2021Deep Hedging: Learning to Remove the Drift under Trading Frictions with Minimal Equivalent Near-Martingale Measurescited by 11
  5. 2022Fractional SDE-Net: Generation of Time Series Data with Long-term Memorycited by 9
  6. 2021Scenario generation for market risk models using generative neural networkscited by 7
  7. 2023FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEscited by 6
  8. 2022Simulating financial time series using attentioncited by 4
  9. 2024Universal randomised signatures for generative time series modellingcited by 4
  10. 2025Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chancecited by 4
  11. 2022Deep Learning for Systemic Risk Measurescited by 3
  12. 2021Generative Adversarial Network: Some Analytical Perspectivescited by 2
13 more
  1. 2021Convergence of GANs Training: A Game and Stochastic Control Methodologycited by 2
  2. 2022Estimating risks of option books using neural-SDE market modelscited by 2
  3. 2022Risk-Neutral Market Simulationcited by 2
  4. 2023Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structurescited by 2
  5. 2025Predicting Liquidity-Aware Bond Yields using Causal GANs and Deep Reinforcement Learning with LLM Evaluationcited by 2
  6. 2022Quantifying Quality of Class-Conditional Generative Models in Time-Series Domaincited by 1
  7. 2023Deep Learning for Mean Field Games with non-separable Hamiltonianscited by 1
  8. 2025Uncertainty-Aware Strategies: A Model-Agnostic Framework for Robust Financial Optimization through Subsamplingcited by 1
  9. 2026Generating Financial Time Series by Matching Random Convolutional Featurescited by 1
  10. 2026DYSANOS Generative Dynamic Smooth Arbitrage-free Non-parametric Option Surfacescited by 1
  11. 2021Deep Hedging: Learning Risk-Neutral Implied Volatility Dynamicscited by 0
  12. 2024Trading Devil Final: Backdoor attack via Stock market and Bayesian Optimizationcited by 0
  13. 2025STEB: In Search of the Best Evaluation Approach for Synthetic Time Seriescited by 0