IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1901.08943.html
   My bibliography  Save this paper

Pricing options and computing implied volatilities using neural networks

Author

Listed:
  • Shuaiqiang Liu
  • Cornelis W. Oosterlee
  • Sander M. Bohte

Abstract

This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of accelerating the corresponding numerical methods. With ANNs being universal function approximators, this method trains an optimized ANN on a data set generated by a sophisticated financial model, and runs the trained ANN as an agent of the original solver in a fast and efficient way. We test this approach on three different types of solvers, including the analytic solution for the Black-Scholes equation, the COS method for the Heston stochastic volatility model and Brent's iterative root-finding method for the calculation of implied volatilities. The numerical results show that the ANN solver can reduce the computing time significantly.

Suggested Citation

  • Shuaiqiang Liu & Cornelis W. Oosterlee & Sander M. Bohte, 2019. "Pricing options and computing implied volatilities using neural networks," Papers 1901.08943, arXiv.org, revised Apr 2019.
    • Handle: RePEc:arx:papers:1901.08943
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1901.08943
    File Function: Latest version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Rama Cont & Jose da Fonseca, 2002. "Dynamics of implied volatility surfaces," Quantitative Finance, Taylor & Francis Journals, vol. 2(1), pages 45-60.
    2. Yao, Jingtao & Li, Yili & Tan, Chew Lim, 2000. "Option price forecasting using neural networks," Omega, Elsevier, vol. 28(4), pages 455-466, August.
    3. 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.
    4. Fan, Jianqing & Mancini, Loriano, 2009. "Option Pricing With Model-Guided Nonparametric Methods," Journal of the American Statistical Association, American Statistical Association, vol. 104(488), pages 1351-1372.
    5. 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.
    6. 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.
    7. Justin Sirignano & Konstantinos Spiliopoulos, 2017. "DGM: A deep learning algorithm for solving partial differential equations," Papers 1708.07469, arXiv.org, revised Sep 2018.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Johannes Ruf & Weiguan Wang, 2019. "Neural networks for option pricing and hedging: a literature review," Papers 1911.05620, arXiv.org, revised May 2020.
    2. Cao, Yi & Liu, Xiaoquan & Zhai, Jia, 2021. "Option valuation under no-arbitrage constraints with neural networks," European Journal of Operational Research, Elsevier, vol. 293(1), pages 361-374.
    3. Yanhui Shen, 2023. "American Option Pricing using Self-Attention GRU and Shapley Value Interpretation," Papers 2310.12500, arXiv.org.
    4. 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.
    5. Liu, Xiaoquan & Cao, Yi & Ma, Chenghu & Shen, Liya, 2019. "Wavelet-based option pricing: An empirical study," European Journal of Operational Research, Elsevier, vol. 272(3), pages 1132-1142.
    6. Ke Nian & Thomas F. Coleman & Yuying Li, 2018. "Learning minimum variance discrete hedging directly from the market," Quantitative Finance, Taylor & Francis Journals, vol. 18(7), pages 1115-1128, July.
    7. Panayiotis Andreou & Chris Charalambous & Spiros Martzoukos, 2006. "Robust Artificial Neural Networks for Pricing of European Options," Computational Economics, Springer;Society for Computational Economics, vol. 27(2), pages 329-351, May.
    8. Anindya Goswami & Nimit Rana, 2024. "A market resilient data-driven approach to option pricing," Papers 2409.08205, arXiv.org.
    9. 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.
    10. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Deep Stochastic Optimization in Finance," Papers 2205.04604, arXiv.org.
    11. A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2023. "Deep stochastic optimization in finance," Digital Finance, Springer, vol. 5(1), pages 91-111, March.
    12. M. Ryan Haley & Todd B. Walker, 2010. "Alternative tilts for nonparametric option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 30(10), pages 983-1006, October.
    13. Bodo Herzog & Sufyan Osamah, 2019. "Reverse Engineering of Option Pricing: An AI Application," IJFS, MDPI, vol. 7(4), pages 1-12, November.
    14. Julia Bennell & Charles Sutcliffe, 2004. "Black–Scholes versus artificial neural networks in pricing FTSE 100 options," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 12(4), pages 243-260, October.
    15. Christian Bayer & Blanka Horvath & Aitor Muguruza & Benjamin Stemper & Mehdi Tomas, 2019. "On deep calibration of (rough) stochastic volatility models," Papers 1908.08806, arXiv.org.
    16. Mark T. Leung & An‐Sing Chen & Ruben Mancha, 2009. "Making trading decisions for financial‐engineered derivatives: a novel ensemble of neural networks using information content," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 16(4), pages 257-277, October.
    17. Yi-Hsien Wang, 2009. "Using neural network to forecast stock index option price: a new hybrid GARCH approach," Quality & Quantity: International Journal of Methodology, Springer, vol. 43(5), pages 833-843, September.
    18. Yao Wang & Jingmei Zhao & Qing Li & Xiangyu Wei, 2024. "Considering momentum spillover effects via graph neural network in option pricing," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 44(6), pages 1069-1094, June.
    19. Jaegi Jeon & Kyunghyun Park & Jeonggyu Huh, 2021. "Extensive networks would eliminate the demand for pricing formulas," Papers 2101.09064, arXiv.org.
    20. Masanori Hirano & Kentaro Imajo & Kentaro Minami & Takuya Shimada, 2023. "Efficient Learning of Nested Deep Hedging using Multiple Options," Papers 2305.12264, arXiv.org.

    More about this item

    JEL classification:

    • C - Mathematical and Quantitative Methods
    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets
    • G2 - Financial Economics - - Financial Institutions and Services
    • G3 - Financial Economics - - Corporate Finance and Governance
    • M2 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Economics
    • M4 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Accounting
    • K2 - Law and Economics - - Regulation and Business Law

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1901.08943. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.