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Pricing options and computing implied volatilities using neural networks

Citations

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Cited by:

  1. Duosi Zheng & Hanzhong Guo & Yanchu Liu & Wei Huang, 2025. "Neural Jumps for Option Pricing," Papers 2506.05137, arXiv.org.
  2. Jaegi Jeon & Kyunghyun Park & Jeonggyu Huh, 2021. "Extensive networks would eliminate the demand for pricing formulas," Papers 2101.09064, arXiv.org.
  3. Geon Lee & Tae-Kyoung Kim & Hyun-Gyoon Kim & Jeonggyu Huh, 2022. "Newton–Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities," JRFM, MDPI, vol. 15(12), pages 1-8, December.
  4. Zhaoyi Xu & Yuqing Zeng & Yangrong Xue & Shenggang Yang, 2022. "Early Warning of Chinese Yuan’s Exchange Rate Fluctuation and Value at Risk Measure Using Neural Network Joint Optimization Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 60(4), pages 1293-1315, December.
  5. Antal Ratku & Dirk Neumann, 2022. "Derivatives of feed-forward neural networks and their application in real-time market risk management," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(3), pages 947-965, September.
  6. Sarit Maitra & Vivek Mishra & Goutam Kr. Kundu & Kapil Arora, 2023. "Integration of Fractional Order Black-Scholes Merton with Neural Network," Papers 2310.04464, arXiv.org, revised Oct 2023.
  7. Patrick Büchel & Michael Kratochwil & Maximilian Nagl & Daniel Rösch, 2022. "Deep calibration of financial models: turning theory into practice," Review of Derivatives Research, Springer, vol. 25(2), pages 109-136, July.
  8. Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020. "Neural Network Pricing of American Put Options," Risks, MDPI, vol. 8(3), pages 1-24, July.
  9. Giacomo Morelli & Lea Petrella, 2021. "Option Pricing, Zero Lower Bound, and COVID-19," Risks, MDPI, vol. 9(9), pages 1-13, September.
  10. Junike, Gero & Pankrashkin, Konstantin, 2022. "Precise option pricing by the COS method—How to choose the truncation range," Applied Mathematics and Computation, Elsevier, vol. 421(C).
  11. Amine M. Aboussalah & Xuanze Li & Cheng Chi & Raj Patel, 2024. "The AI Black-Scholes: Finance-Informed Neural Network," Papers 2412.12213, arXiv.org.
  12. Ashley Davey & Harry Zheng, 2022. "Deep Learning for Constrained Utility Maximisation," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 661-692, June.
  13. Michele Mininni & Giuseppe Orlando & Giovanni Taglialatela, 2021. "Challenges in approximating the Black and Scholes call formula with hyperbolic tangents," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(1), pages 73-100, June.
  14. Li, Pengshi & Lin, Yan & Yu, Xing & Liu, Guifang, 2025. "Does bid-ask spread explains the smile? On DVF and DML," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
  15. Kevin Jakob & Johannes Churt & Matthias Fischer & Kim Nolte & Yarema Okhrin & Dirk Sondermann & Stefan Wilke & Thomas Worbs, 2023. "Fast approximation methods for credit portfolio risk calculations," Digital Finance, Springer, vol. 5(3), pages 689-716, December.
  16. Sangseop Lim & Chang-hee Lee & Won-Ju Lee & Junghwan Choi & Dongho Jung & Younghun Jeon, 2022. "Valuation of the Extension Option in Time Charter Contracts in the LNG Market," Energies, MDPI, vol. 15(18), pages 1-14, September.
  17. Gero Junike & Konstantin Pankrashkin, 2021. "Precise option pricing by the COS method--How to choose the truncation range," Papers 2109.01030, arXiv.org, revised Jan 2022.
  18. Ryno du Plooy & Pierre J. Venter, 2021. "A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework," JRFM, MDPI, vol. 14(6), pages 1-18, June.
  19. Glau, Kathrin & Wunderlich, Linus, 2022. "The deep parametric PDE method and applications to option pricing," Applied Mathematics and Computation, Elsevier, vol. 432(C).
  20. Anna Clevenhaus & Claudia Totzeck & Matthias Ehrhardt, 2025. "A Space Mapping approach for the calibration of financial models with the application to the Heston model," Papers 2501.14521, arXiv.org.
  21. Timothy DeLise, 2021. "Neural Options Pricing," Papers 2105.13320, arXiv.org.
  22. Lokeshwar, Vikranth & Bharadwaj, Vikram & Jain, Shashi, 2022. "Explainable neural network for pricing and universal static hedging of contingent claims," Applied Mathematics and Computation, Elsevier, vol. 417(C).
  23. Ashley Davey & Harry Zheng, 2020. "Deep Learning for Constrained Utility Maximisation," Papers 2008.11757, arXiv.org, revised Aug 2021.
  24. Geon Lee & Tae-Kyoung Kim & Hyun-Gyoon Kim & Jeonggyu Huh, 2022. "Newton Raphson Emulation Network for Highly Efficient Computation of Numerous Implied Volatilities," Papers 2210.15969, arXiv.org.
  25. Shuaiqiang Liu & Anastasia Borovykh & Lech A. Grzelak & Cornelis W. Oosterlee, 2019. "A neural network-based framework for financial model calibration," Papers 1904.10523, arXiv.org.
  26. Vincenzo Candila & Lucio Palazzo, 2020. "Neural Networks and Betting Strategies for Tennis," Risks, MDPI, vol. 8(3), pages 1-19, June.
  27. Lei Zhao & Lin Cai & Wu-Sheng Lu, 2025. "Adaptive Nesterov Accelerated Distributional Deep Hedging for Efficient Volatility Risk Management," Papers 2502.17777, arXiv.org.
  28. Roman V. Ivanov, 2023. "On the Stochastic Volatility in the Generalized Black-Scholes-Merton Model," Risks, MDPI, vol. 11(6), pages 1-23, June.
  29. Xiaoyu Shen & Fang Fang & Chengguang Liu, 2024. "The Fourier Cosine Method for Discrete Probability Distributions," Papers 2410.04487, arXiv.org, revised Oct 2024.
  30. Ballotta, Laura & Rayée, Grégory, 2022. "Smiles & smirks: Volatility and leverage by jumps," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1145-1161.
  31. Serena Della Corte & Laurens Van Mieghem & Antonis Papapantoleon & Jonas Papazoglou-Hennig, 2023. "Machine learning for option pricing: an empirical investigation of network architectures," Papers 2307.07657, arXiv.org, revised Dec 2025.
  32. Beatriz Salvador & Cornelis W. Oosterlee & Remco van der Meer, 2020. "Financial Option Valuation by Unsupervised Learning with Artificial Neural Networks," Mathematics, MDPI, vol. 9(1), pages 1-20, December.
  33. S. Sapna & Biju R. Mohan, 2024. "Comparative Analysis of Root Finding Algorithms for Implied Volatility Estimation of Ethereum Options," Computational Economics, Springer;Society for Computational Economics, vol. 64(1), pages 515-550, July.
  34. Akanksha Sharma & Chandan Kumar Verma & Priya Singh, 2025. "Enhancing Option Pricing Accuracy in the Indian Market: A CNN-BiLSTM Approach," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3751-3778, June.
  35. Laura Leal & Mathieu Lauri`ere & Charles-Albert Lehalle, 2020. "Learning a functional control for high-frequency finance," Papers 2006.09611, arXiv.org, revised Feb 2021.
  36. Jasper Rou, 2025. "Error Analysis of Deep PDE Solvers for Option Pricing," Papers 2505.05121, arXiv.org.
  37. Kathrin Glau & Linus Wunderlich, 2020. "The Deep Parametric PDE Method: Application to Option Pricing," Papers 2012.06211, arXiv.org.
  38. Nikita Medvedev & Zhiguang Wang, 2022. "Multistep forecast of the implied volatility surface using deep learning," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 42(4), pages 645-667, April.
  39. Lijie Ding & Egang Lu & Kin Cheung, 2025. "Deep Learning Option Pricing with Market Implied Volatility Surfaces," Papers 2509.05911, arXiv.org.
  40. Noshaba Zulfiqar & Saqib Gulzar, 2021. "Implied volatility estimation of bitcoin options and the stylized facts of option pricing," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-30, December.
  41. S'andor Kuns'agi-M'at'e & G'abor F'ath & Istv'an Csabai & G'abor Moln'ar-S'aska, 2022. "Deep Weighted Monte Carlo: A hybrid option pricing framework using neural networks," Papers 2208.14038, arXiv.org, revised Dec 2022.
  42. Shuaiqiang Liu & 'Alvaro Leitao & Anastasia Borovykh & Cornelis W. Oosterlee, 2020. "On Calibration Neural Networks for extracting implied information from American options," Papers 2001.11786, arXiv.org.
  43. Maciej Wysocki & Robert Ślepaczuk, 2020. "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers 2020-19, Faculty of Economic Sciences, University of Warsaw.
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