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Options pricing under the one-dimensional jump-diffusion model using the radial basis function interpolation scheme

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  • Ron Chan
  • Simon Hubbert

Abstract

This paper will demonstrate how European and American option prices can be computed under the jump-diffusion model using the radial basis function (RBF) interpolation scheme. The RBF interpolation scheme is demonstrated by solving an option pricing formula, a one-dimensional partial integro-differential equation (PIDE). We select the cubic spline radial basis function and adopt a simple numerical algorithm (Briani et al. in Calcolo 44:33–57, 2007 ) to establish a finite computational range for the improper integral of the PIDE. This algorithm reduces the truncation error of approximating the improper integral. As a result, we are able to achieve a higher approximation accuracy of the integral with the application of any quadrature. Moreover, we a numerical technique termed cubic spline factorisation (Bos and Salkauskas in J Approx Theory 51:81–88, 1987 ) to solve the inversion of an ill-conditioned RBF interpolant, which is a well-known research problem in the RBF field. Finally, our numerical experiments show that in the European case, our RBF-interpolation solution is second-order accurate for spatial variables, while in the American case, it is second-order accurate for spatial variables and first-order accurate for time variables. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Ron Chan & Simon Hubbert, 2014. "Options pricing under the one-dimensional jump-diffusion model using the radial basis function interpolation scheme," Review of Derivatives Research, Springer, vol. 17(2), pages 161-189, July.
  • Handle: RePEc:kap:revdev:v:17:y:2014:i:2:p:161-189
    DOI: 10.1007/s11147-013-9095-3
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    Cited by:

    1. Anna Maria Gambaro & Nicola Secomandi, 2021. "A Discussion of Non‐Gaussian Price Processes for Energy and Commodity Operations," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 47-67, January.
    2. Maximilian Ga{ss} & Kathrin Glau, 2016. "A Flexible Galerkin Scheme for Option Pricing in L\'evy Models," Papers 1603.08216, arXiv.org.
    3. Ron Tat Lung Chan, 2016. "Adaptive Radial Basis Function Methods for Pricing Options Under Jump-Diffusion Models," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 623-643, April.
    4. Yusho Kagraoka, 2020. "The Fractional Step Method versus the Radial Basis Functions for Option Pricing with Correlated Stochastic Processes," IJFS, MDPI, vol. 8(4), pages 1-13, December.
    5. Hatem Ben-Ameur & Rim Chérif & Bruno Rémillard, 2016. "American-style options in jump-diffusion models: estimation and evaluation," Quantitative Finance, Taylor & Francis Journals, vol. 16(8), pages 1313-1324, August.
    6. Shirzadi, Mohammad & Rostami, Mohammadreza & Dehghan, Mehdi & Li, Xiaolin, 2023. "American options pricing under regime-switching jump-diffusion models with meshfree finite point method," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    7. Chan, Tat Lung (Ron), 2019. "Efficient computation of european option prices and their sensitivities with the complex fourier series method," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    8. Weiwei Liu & Zhile Yang & Kexin Bi, 2017. "Forecasting the Acquisition of University Spin-Outs: An RBF Neural Network Approach," Complexity, Hindawi, vol. 2017, pages 1-8, October.

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    More about this item

    Keywords

    European options; American options; Jump-diffusion models; Radial basis functions; Cubic spline; C6; G12; G13;
    All these keywords.

    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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