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A Low-Bias Simulation Scheme For The Sabr Stochastic Volatility Model

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  • BIN CHEN

    (CWI, Center for Mathematics and Computer Science, Amsterdam, The Netherlands;
    Derivative Research & Validation, Rabobank International, Utrecht, The Netherlands)

  • CORNELIS W. OOSTERLEE

    (CWI, Center for Mathematics and Computer Science, Amsterdam, The Netherlands)

  • HANS VAN DER WEIDE

    (Faculty of Electrical Engineering, Mathematics and Computer Science, TU Delft, The Netherlands)

Abstract

The Stochastic Alpha Beta Rho Stochastic Volatility (SABR-SV) model is widely used in the financial industry for the pricing of fixed income instruments. In this paper we develop a low-bias simulation scheme for the SABR-SV model, which deals efficiently with (undesired) possible negative values in the asset price process, the martingale property of the discrete scheme and the discretization bias of commonly used Euler discretization schemes. The proposed algorithm is based the analytic properties of the governing distribution. Experiments with realistic model parameters show that this scheme is robust for interest rate valuation.

Suggested Citation

  • Bin Chen & Cornelis W. Oosterlee & Hans Van Der Weide, 2012. "A Low-Bias Simulation Scheme For The Sabr Stochastic Volatility Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(02), pages 1-37.
    • Handle: RePEc:wsi:ijtafx:v:15:y:2012:i:02:n:s0219024912500161
    DOI: 10.1142/S0219024912500161
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    References listed on IDEAS

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    1. Ola Elerian, 1998. "A note on the existence of a closed form conditional transition density for the Milstein scheme," Economics Series Working Papers 1998-W18, University of Oxford, Department of Economics.
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    Cited by:

    1. Jaehyuk Choi & Byoung Ki Seo, 2023. "Option pricing under the normal SABR model with Gaussian quadratures," Papers 2301.02797, arXiv.org.
    2. 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.
    3. Julio Guerrero & Giuseppe Orlando, 2022. "Stochastic Local Volatility models and the Wei-Norman factorization method," Papers 2201.11241, arXiv.org.
    4. Ning Cai & Yingda Song & Nan Chen, 2017. "Exact Simulation of the SABR Model," Operations Research, INFORMS, vol. 65(4), pages 931-951, August.
    5. Choi, Jaehyuk & Wu, Lixin, 2021. "The equivalent constant-elasticity-of-variance (CEV) volatility of the stochastic-alpha-beta-rho (SABR) model," Journal of Economic Dynamics and Control, Elsevier, vol. 128(C).
    6. Christian Bayer & Peter Friz & Ronnie Loeffen, 2012. "Semi-closed form cubature and applications to financial diffusion models," Quantitative Finance, Taylor & Francis Journals, vol. 13(5), pages 769-782, October.
    7. 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.
    8. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2021. "Efficient simulation of generalized SABR and stochastic local volatility models based on Markov chain approximations," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1046-1062.
    9. Ralph Rudd & Thomas A. McWalter & Joerg Kienitz & Eckhard Platen, 2017. "Fast Quantization of Stochastic Volatility Models," Papers 1704.06388, arXiv.org.
    10. Leitao, Álvaro & Grzelak, Lech A. & Oosterlee, Cornelis W., 2017. "On a one time-step Monte Carlo simulation approach of the SABR model: Application to European options," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 461-479.
    11. Blanka Horvath & Oleg Reichmann, 2018. "Dirichlet Forms and Finite Element Methods for the SABR Model," Papers 1801.02719, arXiv.org.

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