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Application of kernel-based stochastic gradient algorithms to option pricing

Author

Listed:
  • Barty Kengy

    (EDF R&D, 1 avenue du Général de Gaulle, 92141 Clamart Cedex, France. Email: kengy.barty@edf.fr)

  • Girardeau Pierre

    (EDF R&D, also with the École Nationale des Ponts et Chaussées (ENPC) and the École Nationale Supérieure de Techniques Avancées (ENSTA), France. Email: pierre.girardeau@cermics.enpc.fr)

  • Strugarek Cyrille

    (EDF R&D when this research was done also, currently at Calyon, France. Email: cyrille.strugarek@calyon.com)

  • Roy Jean-Sébastien

Abstract

We present an algorithm for American option pricing based on stochastic approximation techniques. Besides working on a finite subset of the exercise dates (e.g. considering the associated Bermudean option), option pricing algorithms generally involve another step of discretization, either on the state space or on the underlying functional space. Our work, which is an application of a more general perturbed gradient algorithm introduced recently by the authors, consists in approximating the value functions of the classical dynamic programming equation at each time step by a linear combination of kernels. The so-called kernel-based stochastic gradient algorithm avoids any a priori discretization, besides the discretization of time. Thus, it converges toward the optimum of the non-discretized Bermudan option pricing problem.We present a comprehensive methodology to implement efficiently this algorithm, including discussions on the numerical tools used, like the Fast Gauss Transform, or Brownian bridge.We also compare our results to some existing methods, and provide empirical statistical results.

Suggested Citation

  • Barty Kengy & Girardeau Pierre & Strugarek Cyrille & Roy Jean-Sébastien, 2008. "Application of kernel-based stochastic gradient algorithms to option pricing," Monte Carlo Methods and Applications, De Gruyter, vol. 14(2), pages 99-127, January.
    • Handle: RePEc:bpj:mcmeap:v:14:y:2008:i:2:p:99-127:n:1
    DOI: 10.1515/MCMA.2008.006
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    References listed on IDEAS

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    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    3. Eric Fournié & Jean-Michel Lasry & Pierre-Louis Lions & Jérôme Lebuchoux, 2001. "Applications of Malliavin calculus to Monte-Carlo methods in finance. II," Finance and Stochastics, Springer, vol. 5(2), pages 201-236.
    4. Kengy Barty & Jean-Sébastien Roy & Cyrille Strugarek, 2007. "Hilbert-Valued Perturbed Subgradient Algorithms," Mathematics of Operations Research, INFORMS, vol. 32(3), pages 551-562, August.
    5. Barraquand, Jérôme & Martineau, Didier, 1995. "Numerical Valuation of High Dimensional Multivariate American Securities," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 30(3), pages 383-405, September.
    6. Chen, Xiaohong & White, Halbert, 1998. "Nonparametric Adaptive Learning with Feedback," Journal of Economic Theory, Elsevier, vol. 82(1), pages 190-222, September.
    7. Jèôme Barraquand, 1995. "Numerical Valuation of High Dimensional Multivariate European Securities," Management Science, INFORMS, vol. 41(12), pages 1882-1891, December.
    8. 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.
    9. Eric Fournié & Jean-Michel Lasry & Pierre-Louis Lions & Jérôme Lebuchoux & Nizar Touzi, 1999. "Applications of Malliavin calculus to Monte Carlo methods in finance," Finance and Stochastics, Springer, vol. 3(4), pages 391-412.
    10. Vlad Bally & Gilles Pagès & Jacques Printems, 2005. "A Quantization Tree Method For Pricing And Hedging Multidimensional American Options," Mathematical Finance, Wiley Blackwell, vol. 15(1), pages 119-168, January.
    11. Mark Broadie & Yusaku Yamamoto, 2003. "Application of the Fast Gauss Transform to Option Pricing," Management Science, INFORMS, vol. 49(8), pages 1071-1088, August.
    12. Bernard Lapeyre & Emmanuel Temam, 2001. "Competitive Monte Carlo methods for the pricing of Asian options," Post-Print hal-01667057, HAL.
    13. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
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    Cited by:

    1. 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.

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