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Machine learning for pricing American options in high-dimensional Markovian and non-Markovian models

Citations

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Cited by:

  1. Etienne Chevalier & Sergio Pulido & Elizabeth Zúñiga, 2021. "American options in the Volterra Heston model," Working Papers hal-03178306, HAL.
  2. Ludovic Goudenège & Andrea Molent & Antonino Zanette, 2021. "Gaussian process regression for pricing variable annuities with stochastic volatility and interest rate," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 57-72, June.
  3. Florian Bourgey & Stefano De Marco & Peter K. Friz & Paolo Pigato, 2023. "Local volatility under rough volatility," Mathematical Finance, Wiley Blackwell, vol. 33(4), pages 1119-1145, October.
  4. Yang, Wensheng & Ma, Jingtang & Cui, Zhenyu, 2025. "A general valuation framework for rough stochastic local volatility models and applications," European Journal of Operational Research, Elsevier, vol. 322(1), pages 307-324.
  5. Peter K. Friz & Paul Gassiat & Paolo Pigato, 2022. "Short-dated smile under rough volatility: asymptotics and numerics," Quantitative Finance, Taylor & Francis Journals, vol. 22(3), pages 463-480, March.
  6. Jirong Zhuang & Xuan Wu, 2025. "SABR-Informed Multitask Gaussian Process: A Synthetic-to-Real Framework for Implied Volatility Surface Construction," Papers 2506.22888, arXiv.org, revised Feb 2026.
  7. Leonardo Kanashiro Felizardo & Elia Matsumoto & Emilio Del-Moral-Hernandez, 2022. "Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise," Papers 2208.00765, arXiv.org.
  8. Christian Bayer & Jinniao Qiu & Yao Yao, 2020. "Pricing Options Under Rough Volatility with Backward SPDEs," Papers 2008.01241, arXiv.org.
  9. Etienne Chevalier & Sergio Pulido & Elizabeth Z'u~niga, 2021. "American options in the Volterra Heston model," Papers 2103.11734, arXiv.org, revised May 2022.
  10. Etienne Chevalier & Sergio Pulido & Elizabeth Zúñiga, 2022. "American options in the Volterra Heston model," Post-Print hal-03178306, HAL.
  11. Mike Ludkovski, 2020. "mlOSP: Towards a Unified Implementation of Regression Monte Carlo Algorithms," Papers 2012.00729, arXiv.org, revised Oct 2022.
  12. Christian Bayer & Luca Pelizzari & John Schoenmakers, 2023. "Primal and dual optimal stopping with signatures," Papers 2312.03444, arXiv.org, revised Feb 2025.
  13. Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2022. "Computing XVA for American basket derivatives by Machine Learning techniques," Papers 2209.06485, arXiv.org.
  14. Junyi Ye & Bhaskar Goswami & Jingyi Gu & Ajim Uddin & Guiling Wang, 2024. "From Factor Models to Deep Learning: Machine Learning in Reshaping Empirical Asset Pricing," Papers 2403.06779, arXiv.org.
  15. Jingtang Ma & Wensheng Yang & Zhenyu Cui, 2021. "Semimartingale and continuous-time Markov chain approximation for rough stochastic local volatility models," Papers 2110.08320, arXiv.org, revised Oct 2021.
  16. Brini, Alessio & Lenz, Jimmie, 2024. "Pricing cryptocurrency options with machine learning regression for handling market volatility," Economic Modelling, Elsevier, vol. 136(C).
  17. Christian Bayer & Luca Pelizzari & John Schoenmakers, 2025. "Primal and dual optimal stopping with signatures," Finance and Stochastics, Springer, vol. 29(4), pages 981-1014, October.
  18. Alessio Brini & David A. Hsieh & Patrick Kuiper & Sean Moushegian & David Ye, 2025. "Empirical Models of the Time Evolution of SPX Option Prices," Papers 2506.17511, arXiv.org.
  19. Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2021. "Moving average options: Machine Learning and Gauss-Hermite quadrature for a double non-Markovian problem," Papers 2108.11141, arXiv.org.
  20. Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2024. "Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model," Papers 2407.13213, arXiv.org, revised Jun 2025.
  21. Bhaskar Goswami & Ajim Uddin, 2026. "Significance of predictors: revisiting stock return predictions using explainable AI," Annals of Operations Research, Springer, vol. 357(1), pages 223-257, February.
  22. Jirong Zhuang & Deng Ding & Weiguo Lu & Xuan Wu & Gangnan Yuan, 2025. "A Gaussian Process Based Method with Deep Kernel Learning for Pricing High-Dimensional American Options," Computational Economics, Springer;Society for Computational Economics, vol. 66(5), pages 3687-3708, November.
  23. Andrea Della Vecchia & Damir Filipovi'c, 2025. "Error Propagation in Dynamic Programming: From Stochastic Control to Option Pricing," Papers 2509.20239, arXiv.org.
  24. Johan Auster & Ludovic Mathys & Fabio Maeder, 2021. "JDOI Variance Reduction Method and the Pricing of American-Style Options," Papers 2104.01365, arXiv.org, revised May 2021.
  25. Lotfi Boudabsa & Damir Filipovi'c, 2022. "Ensemble learning for portfolio valuation and risk management," Papers 2204.05926, arXiv.org.
  26. Bradley Sturt, 2021. "A nonparametric algorithm for optimal stopping based on robust optimization," Papers 2103.03300, arXiv.org, revised Mar 2023.
  27. Georgy Milyushkov, 2025. "Can Machine Learning Algorithms Outperform Traditional Models for Option Pricing?," Papers 2510.01446, arXiv.org.
  28. Goudenège, Ludovic & Molent, Andrea & Zanette, Antonino, 2022. "Moving average options: Machine learning and Gauss-Hermite quadrature for a double non-Markovian problem," European Journal of Operational Research, Elsevier, vol. 303(2), pages 958-974.
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