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Deep Local Volatility

Author

Listed:
  • Marc Chataigner

    (Department of Mathematics, University of Evry, Paris Saclay, 91100 Essonne, France
    Ph.D. student under the supervision of S. Crépey. The Ph.D. thesis of Marc Chataigner is co-funded by the Research Initiative “Modélisation des marchés actions, obligations et dérivés”, financed by HSBC France under the aegis of the Europlace Institute of Finance, and by a public grant as part of investissement d’avenir project, reference ANR-11-LABX-0056-LLH LabEx LMH. The views and opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views or policies of HSBC Investment Bank, its subsidiaries or affiliates.)

  • Stéphane Crépey

    (Department of Mathematics, University of Evry, Paris Saclay, 91100 Essonne, France)

  • Matthew Dixon

    (Department of Applied Mathematics, Illinois Institute of Technology, Chicago, IL 60616-3793, USA
    The research of Matthew Dixon benefited from the support of Intel Corp.)

Abstract

Deep learning for option pricing has emerged as a novel methodology for fast computations with applications in calibration and computation of Greeks. However, many of these approaches do not enforce any no-arbitrage conditions, and the subsequent local volatility surface is never considered. In this article, we develop a deep learning approach for interpolation of European vanilla option prices which jointly yields the full surface of local volatilities. We demonstrate the modification of the loss function or the feed forward network architecture to enforce (hard constraints approach) or favor (soft constraints approach) the no-arbitrage conditions and we specify the experimental design parameters that are needed for adequate performance. A novel component is the use of the Dupire formula to enforce bounds on the local volatility associated with option prices, during the network fitting. Our methodology is benchmarked numerically on real datasets of DAX vanilla options.

Suggested Citation

  • Marc Chataigner & Stéphane Crépey & Matthew Dixon, 2020. "Deep Local Volatility," Risks, MDPI, vol. 8(3), pages 1-18, August.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:3:p:82-:d:393770
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    References listed on IDEAS

    as
    1. 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.
    2. A Itkin, 2019. "Deep learning calibration of option pricing models: some pitfalls and solutions," Papers 1906.03507, arXiv.org.
    3. 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.
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    Citations

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    Cited by:

    1. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2022. "Estimating risks of option books using neural-SDE market models," Papers 2202.07148, arXiv.org.
    2. Marc Chataigner & Areski Cousin & St'ephane Cr'epey & Matthew Dixon & Djibril Gueye, 2022. "Beyond Surrogate Modeling: Learning the Local Volatility Via Shape Constraints," Papers 2212.09957, arXiv.org.
    3. Zhe Wang & Nicolas Privault & Claude Guet, 2021. "Deep self-consistent learning of local volatility," Papers 2201.07880, arXiv.org, revised Nov 2023.
    4. Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021. "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, vol. 3(3), pages 209-248, December.
    5. Samuel N. Cohen & Christoph Reisinger & Sheng Wang, 2021. "Arbitrage-free neural-SDE market models," Papers 2105.11053, arXiv.org, revised Aug 2021.

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