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On an efficient multiple time step Monte Carlo simulation of the SABR model

Author

Listed:
  • Álvaro Leitao
  • Lech A. Grzelak
  • Cornelis W. Oosterlee

Abstract

In this paper, we will present a multiple time step Monte Carlo simulation technique for pricing options under the Stochastic Alpha Beta Rho model. The proposed method is an extension of the one time step Monte Carlo method that we proposed in an accompanying paper Leitao et al. [Appl. Math. Comput. 2017, 293, 461–479], for pricing European options in the context of the model calibration. A highly efficient method results, with many very interesting and nontrivial components, like Fourier inversion for the sum of log-normals, stochastic collocation, Gumbel copula, correlation approximation, that are not yet seen in combination within a Monte Carlo simulation. The present multiple time step Monte Carlo method is especially useful for long-term options and for exotic options.

Suggested Citation

  • Álvaro Leitao & Lech A. Grzelak & Cornelis W. Oosterlee, 2017. "On an efficient multiple time step Monte Carlo simulation of the SABR model," Quantitative Finance, Taylor & Francis Journals, vol. 17(10), pages 1549-1565, October.
    • Handle: RePEc:taf:quantf:v:17:y:2017:i:10:p:1549-1565
    DOI: 10.1080/14697688.2017.1301676
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    References listed on IDEAS

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    1. E. Benhamou, 2001. "Fast Fourier Transform for discrete Asian Options," Computing in Economics and Finance 2001 6, Society for Computational Economics.
    2. Fang, Fang & Oosterlee, Kees, 2008. "A Novel Pricing Method For European Options Based On Fourier-Cosine Series Expansions," MPRA Paper 9319, University Library of Munich, Germany.
    3. Joanne E. Kennedy & Duy Pham, 2014. "On the Approximation of the SABR with Mean Reversion Model: A Probabilistic Approach," Applied Mathematical Finance, Taylor & Francis Journals, vol. 21(5), pages 451-481, November.
    4. Mark Broadie & Özgür Kaya, 2006. "Exact Simulation of Stochastic Volatility and Other Affine Jump Diffusion Processes," Operations Research, INFORMS, vol. 54(2), pages 217-231, April.
    5. repec:bla:jfinan:v:44:y:1989:i:1:p:211-19 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Leonardo Perotti & Lech A. Grzelak, 2021. "Fast Sampling from Time-Integrated Bridges using Deep Learning," Papers 2111.13901, arXiv.org.
    2. Dan Pirjol & Lingjiong Zhu, 2020. "Asymptotics of the time-discretized log-normal SABR model: The implied volatility surface," Papers 2001.09850, arXiv.org, revised Mar 2020.
    3. Jaehyuk Choi & Chenru Liu & Byoung Ki Seo, 2019. "Hyperbolic normal stochastic volatility model," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 39(2), pages 186-204, February.
    4. Julio Guerrero & Giuseppe Orlando, 2022. "Stochastic Local Volatility models and the Wei-Norman factorization method," Papers 2201.11241, arXiv.org.
    5. Leitao, Álvaro & Oosterlee, Cornelis W. & Ortiz-Gracia, Luis & Bohte, Sander M., 2018. "On the data-driven COS method," Applied Mathematics and Computation, Elsevier, vol. 317(C), pages 68-84.
    6. Blanka Horvath & Aitor Muguruza & Mehdi Tomas, 2019. "Deep Learning Volatility," Papers 1901.09647, arXiv.org, revised Aug 2019.
    7. Christian Bayer & Blanka Horvath & Aitor Muguruza & Benjamin Stemper & Mehdi Tomas, 2019. "On deep calibration of (rough) stochastic volatility models," Papers 1908.08806, arXiv.org.

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