IDEAS home Printed from https://ideas.repec.org/r/eee/phsmap/v527y2019ics0378437119307277.html
   My bibliography  Save this item

Modeling financial time-series with generative adversarial networks

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
  2. Shangguan, Anqi & Xie, Guo & Fei, Rong & Mu, Lingxia & Hei, Xinhong, 2023. "Train wheel degradation generation and prediction based on the time series generation adversarial network," Reliability Engineering and System Safety, Elsevier, vol. 229(C).
  3. Matteo Rizzato & Julien Wallart & Christophe Geissler & Nicolas Morizet & Noureddine Boumlaik, 2023. "Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation," Working Papers hal-03716692, HAL.
  4. Magnus Wiese & Robert Knobloch & Ralf Korn & Peter Kretschmer, 2019. "Quant GANs: Deep Generation of Financial Time Series," Papers 1907.06673, arXiv.org, revised Dec 2019.
  5. Igor Sadoune & Andrea Lodi & Marcelin Joanis, 2022. "Implementing a Hierarchical Deep Learning Approach for Simulating Multi-Level Auction Data," Papers 2207.12255, arXiv.org, revised Feb 2024.
  6. Çelik, Gaffari & Talu, Muhammed Fatih, 2020. "Resizing and cleaning of histopathological images using generative adversarial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
  7. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Applied Economics and Finance, Redfame publishing, vol. 9(3), pages 55-68, August.
  8. 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.
  9. Christophe Geissler & Nicolas Morizet & Matteo Rizzato & Julien Wallart, 2022. "Generative Adversarial Networks Applied to Synthetic Financial Scenarios Generation," Papers 2209.03935, arXiv.org.
  10. Gautier Marti & Victor Goubet & Frank Nielsen, 2021. "cCorrGAN: Conditional Correlation GAN for Learning Empirical Conditional Distributions in the Elliptope," Papers 2107.10606, arXiv.org.
  11. Seyfi, Seyed Mohammad Sina & Sharifi, Azin & Arian, Hamidreza, 2021. "Portfolio Value-at-Risk and expected-shortfall using an efficient simulation approach based on Gaussian Mixture Model," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 190(C), pages 1056-1079.
  12. Rizzato, Matteo & Wallart, Julien & Geissler, Christophe & Morizet, Nicolas & Boumlaik, Noureddine, 2023. "Generative Adversarial Networks applied to synthetic financial scenarios generation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 623(C).
  13. Jungsik Hwang, 2020. "Modeling Financial Time Series using LSTM with Trainable Initial Hidden States," Papers 2007.06848, arXiv.org.
  14. Jos'e-Manuel Pe~na & Fernando Su'arez & Omar Larr'e & Domingo Ram'irez & Arturo Cifuentes, 2023. "A Modified CTGAN-Plus-Features Based Method for Optimal Asset Allocation," Papers 2302.02269, arXiv.org, revised Feb 2023.
  15. Hans Buhler & Blanka Horvath & Terry Lyons & Imanol Perez Arribas & Ben Wood, 2020. "A Data-driven Market Simulator for Small Data Environments," Papers 2006.14498, arXiv.org.
  16. Jun Lu & Danny Ding, 2022. "A Hybrid Approach on Conditional GAN for Portfolio Analysis," Papers 2208.07159, arXiv.org.
  17. Weilong Fu & Ali Hirsa & Jorg Osterrieder, 2022. "Simulating financial time series using attention," Papers 2207.00493, arXiv.org.
  18. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
  19. 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.
  20. Andrea Giuseppe Di Iura & Giulia Terenzi, 2021. "A Bayesian analysis of gain-loss asymmetry," Papers 2104.06044, arXiv.org.
  21. Seyed Mohammad Sina Seyfi & Azin Sharifi & Hamidreza Arian, 2020. "Portfolio Risk Measurement Using a Mixture Simulation Approach," Papers 2011.07994, arXiv.org.
  22. 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.
  23. Jun Lu & Shao Yi, 2022. "Autoencoding Conditional GAN for Portfolio Allocation Diversification," Papers 2207.05701, arXiv.org.
  24. Ye-Sheen Lim & Denise Gorse, 2021. "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow," Papers 2109.13905, arXiv.org.
  25. Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021. "Black-box model risk in finance," Papers 2102.04757, arXiv.org.
  26. Carvajal-Patiño, Daniel & Ramos-Pollán, Raul, 2022. "Synthetic data generation with deep generative models to enhance predictive tasks in trading strategies," Research in International Business and Finance, Elsevier, vol. 62(C).
  27. Andrea Di Iura & Giulia Terenzi, 2022. "A Bayesian analysis of gain-loss asymmetry," SN Business & Economics, Springer, vol. 2(5), pages 1-23, May.
  28. Haoyang Cao & Xin Guo, 2021. "Generative Adversarial Network: Some Analytical Perspectives," Papers 2104.12210, arXiv.org, revised Sep 2021.
  29. Magnus Wiese & Lianjun Bai & Ben Wood & Hans Buehler, 2019. "Deep Hedging: Learning to Simulate Equity Option Markets," Papers 1911.01700, arXiv.org.
  30. Florian Eckerli & Joerg Osterrieder, 2021. "Generative Adversarial Networks in finance: an overview," Papers 2106.06364, arXiv.org, revised Jul 2021.
  31. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
  32. Andrea Coletta & Matteo Prata & Michele Conti & Emanuele Mercanti & Novella Bartolini & Aymeric Moulin & Svitlana Vyetrenko & Tucker Balch, 2021. "Towards Realistic Market Simulations: a Generative Adversarial Networks Approach," Papers 2110.13287, arXiv.org.
  33. Bate He & Eisuke Kita, 2021. "The Application of Sequential Generative Adversarial Networks for Stock Price Prediction," The Review of Socionetwork Strategies, Springer, vol. 15(2), pages 455-470, November.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.