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Neural networks for option pricing and hedging: a literature review

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  • Johannes Ruf
  • Weiguan Wang

Abstract

Neural networks have been used as a nonparametric method for option pricing and hedging since the early 1990s. Far over a hundred papers have been published on this topic. This note intends to provide a comprehensive review. Papers are compared in terms of input features, output variables, benchmark models, performance measures, data partition methods, and underlying assets. Furthermore, related work and regularisation techniques are discussed.

Suggested Citation

  • Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
  • Handle: RePEc:arx:papers:1911.05620
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    References listed on IDEAS

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    1. Dilip B. Madan & Peter P. Carr & Eric C. Chang, 1998. "The Variance Gamma Process and Option Pricing," Review of Finance, European Finance Association, vol. 2(1), pages 79-105.
    2. Marc Sabate Vidales & David Siska & Lukasz Szpruch, 2018. "Unbiased deep solvers for linear parametric PDEs," Papers 1810.05094, arXiv.org, revised Jan 2022.
    3. Andros Gregoriou & Jerome Healy & Christos Ioannidis, 2007. "Hedging under the influence of transaction costs: An empirical investigation on FTSE 100 index options," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 27(5), pages 471-494, May.
    4. Christopher Zapart, 2002. "Stochastic volatility options pricing with wavelets and artificial neural networks," Quantitative Finance, Taylor & Francis Journals, vol. 2(6), pages 487-495.
    5. Charles J. Corrado & Tie Su, 1996. "Skewness And Kurtosis In S&P 500 Index Returns Implied By Option Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 19(2), pages 175-192, June.
    6. Peter Carr & Hélyette Geman & Dilip B. Madan & Marc Yor, 2003. "Stochastic Volatility for Lévy Processes," Mathematical Finance, Wiley Blackwell, vol. 13(3), pages 345-382, July.
    7. S. G. Kou, 2002. "A Jump-Diffusion Model for Option Pricing," Management Science, INFORMS, vol. 48(8), pages 1086-1101, August.
    8. Christian Bayer & Blanka Horvath & Aitor Muguruza & Benjamin Stemper & Mehdi Tomas, 2019. "On deep calibration of (rough) stochastic volatility models," Papers 1908.08806, arXiv.org.
    9. Andreou, Panayiotis C. & Charalambous, Chris & Martzoukos, Spiros H., 2008. "Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1415-1433, March.
    10. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
    11. Hutchinson, James M & Lo, Andrew W & Poggio, Tomaso, 1994. "A Nonparametric Approach to Pricing and Hedging Derivative Securities via Learning Networks," Journal of Finance, American Finance Association, vol. 49(3), pages 851-889, July.
    12. Fei Chen & Charles Sutcliffe, 2012. "Pricing And Hedging Short Sterling Options Using Neural Networks," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 19(2), pages 128-149, April.
    13. repec:czx:journl:v:18:y:2011:i:28:id:189 is not listed on IDEAS
    14. Xu, L. & Dixon, M. & Eales, B.A. & Cai, F.F. & Read, B.J. & Healy, J.V., 2004. "Barrier option pricing: modelling with neural nets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 289-293.
    15. Morelli, Marco J & Montagna, Guido & Nicrosini, Oreste & Treccani, Michele & Farina, Marco & Amato, Paolo, 2004. "Pricing financial derivatives with neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(1), pages 160-165.
    16. Barone-Adesi, Giovanni & Whaley, Robert E, 1987. "Efficient Analytic Approximation of American Option Values," Journal of Finance, American Finance Association, vol. 42(2), pages 301-320, June.
    17. Jim Gatheral & Antoine Jacquier, 2014. "Arbitrage-free SVI volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 14(1), pages 59-71, January.
    18. David Liu & Lei Zhang, 2011. "Pricing Chinese warrants using artificial neural networks coupled with Markov regime switching model," International Journal of Financial Markets and Derivatives, Inderscience Enterprises Ltd, vol. 2(4), pages 314-330.
    19. M. Raberto & G. Cuniberti & M. Riani & E. Scales & F. Mainardi & G. Servizi, 2000. "Learning Short-Option Valuation In The Presence Of Rare Events," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 563-564.
    20. Wan-Ni Lai, 2014. "Comparison of methods to estimate option implied risk-neutral densities," Quantitative Finance, Taylor & Francis Journals, vol. 14(10), pages 1839-1855, October.
    21. 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.
    22. Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008. "Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(13), pages 3192-3200.
    23. Huisu Jang & Jaewook Lee, 2019. "Generative Bayesian neural network model for risk-neutral pricing of American index options," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 587-603, April.
    24. Christian Bayer & Benjamin Stemper, 2018. "Deep calibration of rough stochastic volatility models," Papers 1810.03399, arXiv.org.
    25. A Itkin, 2019. "Deep learning calibration of option pricing models: some pitfalls and solutions," Papers 1906.03507, arXiv.org.
    26. Garcia, Rene & Gencay, Ramazan, 2000. "Pricing and hedging derivative securities with neural networks and a homogeneity hint," Journal of Econometrics, Elsevier, vol. 94(1-2), pages 93-115.
    27. Ryan Ferguson & Andrew Green, 2018. "Deeply Learning Derivatives," Papers 1809.02233, arXiv.org, revised Oct 2018.
    28. Heston, Steven L, 1993. "A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options," The Review of Financial Studies, Society for Financial Studies, vol. 6(2), pages 327-343.
    29. Chris Charalambous & Spiros Martzoukos, 2005. "Hybrid artificial neural networks for efficient valuation of real options and financial derivatives," Computational Management Science, Springer, vol. 2(2), pages 155-161, March.
    30. Nikola Gradojevic & Ramazan Gencay & Dragan Kukolj, 2009. "Option Pricing with Modular Neural Networks," Working Paper series 32_09, Rimini Centre for Economic Analysis.
    31. Healy, J.V. & Dixon, M. & Read, B.J. & Cai, F.F., 2004. "Confidence limits for data mining models of options prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 162-167.
    32. Gunter Meissner & Noriko Kawano, 2001. "Capturing the volatility smile of options on high-tech stocks—A combined GARCH-neural network approach," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 25(3), pages 276-292, September.
    33. A. Carelli & S. Silani & F. Stella, 2000. "Profiling Neural Networks For Option Pricing," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 183-204.
    34. Henry Stone, 2018. "Calibrating rough volatility models: a convolutional neural network approach," Papers 1812.05315, arXiv.org, revised Jul 2019.
    35. Damien Ackerer & Natasa Tagasovska & Thibault Vatter, 2019. "Deep Smoothing of the Implied Volatility Surface," Papers 1906.05065, arXiv.org, revised Oct 2020.
    36. Cox, John C. & Ross, Stephen A. & Rubinstein, Mark, 1979. "Option pricing: A simplified approach," Journal of Financial Economics, Elsevier, vol. 7(3), pages 229-263, September.
    37. Hull, John & White, Alan, 2017. "Optimal delta hedging for options," Journal of Banking & Finance, Elsevier, vol. 82(C), pages 180-190.
    38. repec:dau:papers:123456789/1392 is not listed on IDEAS
    39. Charles J. Corrado & Tie Su, 1996. "Skewness And Kurtosis In S&P 500 Index Returns Implied By Option Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 19(2), pages 175-192, June.
    40. Montagna, Guido & Morelli, Marco & Nicrosini, Oreste & Amato, Paolo & Farina, Marco, 2003. "Pricing derivatives by path integral and neural networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 189-195.
    41. Yoshua Bengio & Joumana Ghosn, 2002. "Multi-Task Learning For Option Pricing," CIRANO Working Papers 2002s-53, CIRANO.
    42. Ali Hirsa & Tugce Karatas & Amir Oskoui, 2019. "Supervised Deep Neural Networks (DNNs) for Pricing/Calibration of Vanilla/Exotic Options Under Various Different Processes," Papers 1902.05810, arXiv.org.
    43. Healy, Jerome V. & Dixon, Maurice & Read, Brian J. & Cai, Fang Fang, 2007. "Non-parametric extraction of implied asset price distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 121-128.
    44. Blanka Horvath & Aitor Muguruza & Mehdi Tomas, 2019. "Deep Learning Volatility," Papers 1901.09647, arXiv.org, revised Aug 2019.
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