mlsanityData to 7 Oct 2026

Quant GANs: Deep Generation of Financial Time Series

arXiv 1907.06673 ↗First posted 15 Jul 2019q-fin.MF cs.LG q-fin.CPcited by 107

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Abstract (arXiv)

Modeling financial time series by stochastic processes is a challenging task and a central area of research in financial mathematics. As an alternative, we introduce Quant GANs, a data-driven model which is inspired by the recent success of generative adversarial networks (GANs). Quant GANs consist of a generator and discriminator function, which utilize temporal convolutional networks (TCNs) and thereby achieve to capture long-range dependencies such as the presence of volatility clusters. The generator function is explicitly constructed such that the induced stochastic process allows a transition to its risk-neutral distribution. Our numerical results highlight that distributional properties for small and large lags are in an excellent agreement and dependence properties such as volatility clusters, leverage effects, and serial autocorrelations can be generated by the generator function of Quant GANs, demonstrably in high fidelity.

Key facts. Cited by 107 papers in the map (data through 7 Oct 2026). First posted on arXiv 15 Jul 2019.

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Citing papers per year: 2021: 19; 2022: 14; 2023: 24; 2024: 10; 2025: 18; 2026: 22.

107 papers.

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  1. 2023Machine Learning for Synthetic Data Generation: A Reviewcited by 118
  2. 2021Recent Advances in Reinforcement Learning in Financecited by 102
  3. 2022Synthetic Data -- what, why and how?cited by 66
  4. 2021Sig-Wasserstein GANs for Time Series Generationcited by 60
  5. 2021PSA-GAN: Progressive Self Attention GANs for Synthetic Time Seriescited by 58
  6. 2023FinDiff: Diffusion Models for Financial Tabular Data Generationcited by 41
  7. 2021Deep Hedging of Derivatives Using Reinforcement Learningcited by 30
  8. 2022HyperTime: Implicit Neural Representation for Time Seriescited by 24
  9. 2023TSGBench: Time Series Generation Benchmarkcited by 24
  10. 2021Pareto GAN: Extending the Representational Power of GANs to Heavy-Tailed Distributionscited by 22
  11. 2022Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformercited by 22
  12. 2021Generative adversarial networks in time series: A survey and taxonomycited by 20
38 more
  1. 2023Generative modeling for time series via Schr"odinger bridgecited by 18
  2. 2021Multi-Asset Spot and Option Market Simulationcited by 17
  3. 2022Distributionally Robust End-to-End Portfolio Constructioncited by 17
  4. 2021A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensingcited by 13
  5. 2023TransFusion: Generating Long, High Fidelity Time Series using Diffusion Models with Transformerscited by 13
  6. 2023AI-Generated Images as Data Source: The Dawn of Synthetic Eracited by 13
  7. 2022Fractional SDE-Net: Generation of Time Series Data with Long-term Memorycited by 9
  8. 2023Synthetic Data as Validationcited by 9
  9. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  10. 2024Time-Causal VAE: Robust Financial Time Series Generatorcited by 8
  11. 2021Scenario generation for market risk models using generative neural networkscited by 7
  12. 2021A Quantum Generative Adversarial Network for distributionscited by 7
  13. 2023A Comprehensive Survey on Generative Diffusion Models for Structured Datacited by 7
  14. 2025Robust time series generation via Schr"odinger Bridge: a comprehensive evaluationcited by 7
  15. 2021How Robust are Limit Order Book Representations under Data Perturbation?cited by 6
  16. 2022Modern Machine Learning Tools for Monitoring and Control of Industrial Processes: A Surveycited by 6
  17. 2023Time-Transformer: Integrating Local and Global Features for Better Time Series Generation (Extended Version)cited by 6
  18. 2024A Financial Time Series Denoiser Based on Diffusion Modelcited by 6
  19. 2022GANs as Gradient Flows that Convergecited by 5
  20. 2022StyleTime: Style Transfer for Synthetic Time Series Generationcited by 5
  21. 2023Adversarial Deep Hedging: Learning to Hedge without Price Process Modelingcited by 5
  22. 2024Unlearnable Examples For Time Seriescited by 5
  23. 2024A Survey of Transformer Enabled Time Series Synthesiscited by 5
  24. 2025New Money: A Systematic Review of Synthetic Data Generation for Financecited by 5
  25. 2026TradeFM: A Generative Foundation Model for Trade-flow and Market Microstructurecited by 5
  26. 2021Time Series (re)sampling using Generative Adversarial Networkscited by 4
  27. 2021Conditional Loss and Deep Euler Scheme for Time Series Generationcited by 4
  28. 2022Simulating financial time series using attentioncited by 4
  29. 2023Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategiescited by 4
  30. 2023Generative Machine Learning for Multivariate Equity Returnscited by 4
  31. 2024Robust Generative Learning with Lipschitz-Regularized α-Divergences Allows Minimal Assumptions on Target Distributionscited by 4
  32. 2024Universal randomised signatures for generative time series modellingcited by 4
  33. 2025Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chancecited by 4
  34. 2025WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Modelscited by 4
  35. 2021Monte Carlo Simulation of SDEs using GANscited by 3
  36. 2022Improving Correlation Capture in Generating Imbalanced Data using Differentially Private Conditional GANscited by 3
  37. 2023Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Networkcited by 3
  38. 2023GAT-GAN : A Graph-Attention-based Time-Series Generative Adversarial Networkcited by 3

50 most cited of 107 All 107 (JSON)