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Quant GANs: deep generation of financial time series

Citations

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Cited by:

  1. Emiel Lemahieu & Kris Boudt & Maarten Wyns, 2023. "Generating drawdown-realistic financial price paths using path signatures," Papers 2309.04507, arXiv.org.
  2. Grilli, Luca & Santoro, Domenico, 2020. "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper 100578, University Library of Munich, Germany.
  3. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "Towards Robust Representation of Limit Orders Books for Deep Learning Models," Papers 2110.05479, arXiv.org, revised Dec 2022.
  4. Sohyeon Kwon & Yongjae Lee, 2024. "Can GANs Learn the Stylized Facts of Financial Time Series?," Papers 2410.09850, arXiv.org.
  5. Seyed Sina Aria & Seyed Hossein Iranmanesh & Hossein Hassani, 2024. "Optimizing Multivariate Time Series Forecasting with Data Augmentation," JRFM, MDPI, vol. 17(11), pages 1-19, October.
  6. Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
  7. Gautier Marti & Victor Goubet & Frank Nielsen, 2021. "cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope," Papers 2107.10606, arXiv.org.
  8. Amine Assouel & Antoine Jacquier & Alexei Kondratyev, 2021. "A Quantum Generative Adversarial Network for distributions," Papers 2110.02742, arXiv.org.
  9. Gero Junike & Solveig Flaig & Ralf Werner, 2023. "Validation of machine learning based scenario generators," Papers 2301.12719, arXiv.org, revised Dec 2024.
  10. Ben Hambly & Renyuan Xu & Huining Yang, 2023. "Recent advances in reinforcement learning in finance," Mathematical Finance, Wiley Blackwell, vol. 33(3), pages 437-503, July.
  11. Zhuohan Wang & Carmine Ventre, 2024. "A Financial Time Series Denoiser Based on Diffusion Model," Papers 2409.02138, arXiv.org.
  12. Hafid Lalioui & Amine Ben Amar & Makram Bellalah, 2025. "Asset Pricing Model in Markets of Imperfect Information and Subjective Views," Papers 2501.11983, arXiv.org, revised Feb 2025.
  13. Jingyi Gu & Fadi P. Deek & Guiling Wang, 2023. "Stock Broad-Index Trend Patterns Learning via Domain Knowledge Informed Generative Network," Papers 2302.14164, arXiv.org.
  14. Blanka Horvath & Josef Teichmann & Zan Zuric, 2021. "Deep Hedging under Rough Volatility," Papers 2102.01962, arXiv.org.
  15. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
  16. Nicolas Boursin & Carl Remlinger & Joseph Mikael & Carol Anne Hargreaves, 2022. "Deep Generators on Commodity Markets; application to Deep Hedging," Papers 2205.13942, arXiv.org.
  17. Jingyi Gu & Wenlu Du & Guiling Wang, 2024. "RAGIC: Risk-Aware Generative Adversarial Model for Stock Interval Construction," Papers 2402.10760, arXiv.org.
  18. Beatrice Acciaio & Anastasis Kratsios & Gudmund Pammer, 2022. "Designing Universal Causal Deep Learning Models: The Geometric (Hyper)Transformer," Papers 2201.13094, arXiv.org, revised Mar 2023.
  19. Magnus Wiese & Phillip Murray & Ralf Korn, 2023. "Sig-Splines: universal approximation and convex calibration of time series generative models," Papers 2307.09767, arXiv.org.
  20. Kohei Hayashi & Kei Nakagawa, 2022. "Fractional SDE-Net: Generation of Time Series Data with Long-term Memory," Papers 2201.05974, arXiv.org, revised Aug 2022.
  21. Song Wei & Andrea Coletta & Svitlana Vyetrenko & Tucker Balch, 2023. "INTAGS: Interactive Agent-Guided Simulation," Papers 2309.01784, arXiv.org, revised Nov 2023.
  22. Shun Chen & Lingling Guo & Lei Ge, 2024. "Increasing the Hong Kong Stock Market Predictability: A Temporal Convolutional Network Approach," Computational Economics, Springer;Society for Computational Economics, vol. 64(5), pages 2853-2878, November.
  23. Luca Grilli & Domenico Santoro, 2022. "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, vol. 19(4), pages 665-681, October.
  24. Solveig Flaig & Gero Junike, 2021. "Scenario generation for market risk models using generative neural networks," Papers 2109.10072, arXiv.org, revised Aug 2023.
  25. Adil Rengim Cetingoz & Charles-Albert Lehalle, 2025. "Synthetic Data for Portfolios: A Throw of the Dice Will Never Abolish Chance," Papers 2501.03993, arXiv.org, revised Jan 2025.
  26. Mohamed Hamdouche & Pierre Henry-Labordere & Huyên Pham, 2023. "Generative modeling for time series via Schrödinger bridge," Working Papers hal-04063041, HAL.
  27. Magnus Wiese & Phillip Murray, 2022. "Risk-Neutral Market Simulation," Papers 2202.13996, arXiv.org.
  28. Nicolas Boursin & Carl Remlinger & Joseph Mikael, 2022. "Deep Generators on Commodity Markets Application to Deep Hedging," Risks, MDPI, vol. 11(1), pages 1-18, December.
  29. Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Jul 2023.
  30. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
  31. Edmond Lezmi & Jules Roche & Thierry Roncalli & Jiali Xu, 2020. "Improving the Robustness of Trading Strategy Backtesting with Boltzmann Machines and Generative Adversarial Networks," Papers 2007.04838, arXiv.org.
  32. Howard Caulfield & James P. Gleeson, 2024. "Systematic comparison of deep generative models applied to multivariate financial time series," Papers 2412.06417, arXiv.org.
  33. Bilgi Yilmaz, 2024. "Housing GANs: Deep Generation of Housing Market Data," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 579-594, July.
  34. Chung I Lu & Julian Sester, 2024. "Generative model for financial time series trained with MMD using a signature kernel," Papers 2407.19848, arXiv.org, revised Dec 2024.
  35. Francesca Biagini & Lukas Gonon & Niklas Walter, 2024. "Universal randomised signatures for generative time series modelling," Papers 2406.10214, arXiv.org, revised Sep 2024.
  36. Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023. "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers 2310.10500, arXiv.org, revised Mar 2024.
  37. Mohamed Hamdouche & Pierre Henry-Labordere & Huy^en Pham, 2023. "Generative modeling for time series via Schr{\"o}dinger bridge," Papers 2304.05093, arXiv.org.
  38. Rama Cont & Mihai Cucuringu & Renyuan Xu & Chao Zhang, 2022. "Tail-GAN: Learning to Simulate Tail Risk Scenarios," Papers 2203.01664, arXiv.org, revised Mar 2023.
  39. Michael Karpe, 2020. "An overall view of key problems in algorithmic trading and recent progress," Papers 2006.05515, arXiv.org.
  40. Masanori Hirano & Kentaro Minami & Kentaro Imajo, 2023. "Adversarial Deep Hedging: Learning to Hedge without Price Process Modeling," Papers 2307.13217, arXiv.org.
  41. Anne-Sophie Krah & Zoran Nikolić & Ralf Korn, 2020. "Least-Squares Monte Carlo for Proxy Modeling in Life Insurance: Neural Networks," Risks, MDPI, vol. 8(4), pages 1-21, November.
  42. Solveig Flaig & Gero Junike, 2022. "Scenario Generation for Market Risk Models Using Generative Neural Networks," Risks, MDPI, vol. 10(11), pages 1-28, October.
  43. Evangelos Liaras & Michail Nerantzidis & Antonios Alexandridis, 2024. "Machine learning in accounting and finance research: a literature review," Review of Quantitative Finance and Accounting, Springer, vol. 63(4), pages 1431-1471, November.
  44. Aleksandar Arandjelovi'c & Julia Eisenberg, 2024. "Reinsurance with neural networks," Papers 2408.06168, arXiv.org.
  45. Konark Jain & Nick Firoozye & Jonathan Kochems & Philip Treleaven, 2024. "Limit Order Book Simulations: A Review," Papers 2402.17359, arXiv.org, revised Mar 2024.
  46. Ivan Guo & Nicolas Langrené & Gregoire Loeper & Wei Ning, 2020. "Robust utility maximization under model uncertainty via a penalization approach," Working Papers hal-02910261, HAL.
  47. Jorino van Rhijn & Cornelis W. Oosterlee & Lech A. Grzelak & Shuaiqiang Liu, 2021. "Monte Carlo Simulation of SDEs using GANs," Papers 2104.01437, arXiv.org.
  48. Xiaoyu Tan & Zili Zhang & Xuejun Zhao & Shuyi Wang, 2022. "DeepPricing: pricing convertible bonds based on financial time-series generative adversarial networks," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-38, December.
  49. Andrew Lesniewski & Giulio Trigila, 2024. "Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics," Papers 2412.00036, arXiv.org, revised Feb 2025.
  50. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
  51. Blanka Horvath & Josef Teichmann & Žan Žurič, 2021. "Deep Hedging under Rough Volatility," Risks, MDPI, vol. 9(7), pages 1-20, July.
  52. Yufei Wu & Mahmoud Mahfouz & Daniele Magazzeni & Manuela Veloso, 2021. "How Robust are Limit Order Book Representations under Data Perturbation?," Papers 2110.04752, arXiv.org.
  53. Alexandre Miot, 2020. "Adversarial trading," Papers 2101.03128, arXiv.org.
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