×

Efficient pricing of options in jump-diffusion models: novel implicit-explicit methods for numerical valuation. (English) Zbl 1540.91091

Summary: This paper presents novel implicit-explicit Runge-Kutta type methods for numerically simulating partial integro-differential equations that arise when pricing options under jump-diffusion models. These methods offer an alternative approach that avoids the need for numerical or analytical inversion of the coefficient matrix. The pricing of European options is formulated as a partial integro-differential equation, while the pricing of American options are treated as a linear complementarity problem. The developed implicit-explicit Runge-Kutta type method is combined with an operator splitting technique to efficiently solve the linear complementarity problem. Stability and convergence analysis of the proposed methods are established using discrete \(\ell^2\)-norm. To validate their efficiency and accuracy, the methods are applied to pricing European and American options under Merton’s and Kou’s models, and the computed results are compared with those reported in the literature.

MSC:

91G60 Numerical methods (including Monte Carlo methods)
65L06 Multistep, Runge-Kutta and extrapolation methods for ordinary differential equations
65M06 Finite difference methods for initial value and initial-boundary value problems involving PDEs
65M12 Stability and convergence of numerical methods for initial value and initial-boundary value problems involving PDEs
91G20 Derivative securities (option pricing, hedging, etc.)
Full Text: DOI

References:

[1] Black, F.; Scholes, M., The pricing of options and corporate liabilities. J. Polit. Econ., 637-654 (1973) · Zbl 1092.91524
[2] Cont, R.; Tankov, P., Financial Modelling with Jump Processes (2004), Chapman & Hall/CRC: Chapman & Hall/CRC Boca Raton, FL · Zbl 1052.91043
[3] Merton, R. C., Option pricing when underlying stock returns are discontinuous. J. Financ. Econ., 125-144 (1976) · Zbl 1131.91344
[4] Kou, S. G., A jump-diffusion model for option pricing. Manage. Sci., 1086-1101 (2002) · Zbl 1216.91039
[5] Bates, D. S., Jumps and stochastic volatility: Exchange rate processes implicit in deutsche mark options. Rev. Financ. Stud., 69-107 (1996)
[6] Andersen, L.; Andereasen, J., Jump-diffusion processes: Volatility smile fitting and numerical methods for option pricing. Rev. Deriv. Res., 231-262 (2000) · Zbl 1274.91398
[7] Maekawa, K.; Lee, S.; Morimoto, T.; ichi Kawai, K., Jump diffusion model with application to the Japanese stock market. Math. Comput. Simulation, 223-236 (2008) · Zbl 1216.91040
[8] Carr, P.; Geman, H.; Madan, D. B.; Yor, M., The fine structure of asset returns: An empirical investigation. J. Bus., 305-332 (2002)
[9] Eberlein, E.; Keller, U.; Prause, K., New insights into smile, mispricing, and value at risk: The hyperbolic model. J. Bus., 371-405 (1998)
[10] in ’t Hout, K., Numerical Partial Differential Equations in Finance Explained (2017), Palgr. Mac.
[11] D’Halluin, Y.; Forsyth, P. A.; Vetzal, K. R., Robust numerical methods for contingent claims under jump diffusion processes. IMA J. Numer. Anal., 87-112 (2005) · Zbl 1134.91405
[12] Almendral, A.; Oosterlee, C. W., Numerical valuation of options with jumps in the underlying. Appl. Numer. Math., 1-18 (2005) · Zbl 1117.91028
[13] Kwon, Y.; Lee, Y., A second-order finite difference method for option pricing under jump-diffusion models. SIAM J. Numer. Anal., 2598-2617 (2011) · Zbl 1232.91712
[14] Briani, M.; Natalini, R.; Russo, G., Implicit-explicit numerical schemes for jump-diffusion processes. Calcolo, 33-57 (2007) · Zbl 1150.65033
[15] Salmi, S.; Toivanen, J., IMEX schemes for pricing options under jump-diffusion models. Appl. Numer. Math., 33-45 (2014) · Zbl 1291.91234
[16] Salmi, S.; Toivanen, J., Comparison and survey of finite difference methods for pricing American options under finite activity jump-diffusion models. Int. J. Comput. Math., 1112-1134 (2012) · Zbl 1255.91410
[17] Kwon, Y.; Lee, Y., A second-order tridiagonal method for American options under jump diffusion models. SIAM J. Sci. Comput., 1860-1872 (2011) · Zbl 1227.91034
[18] Ikonen, S.; Toivanen, J., Operator splitting methods for American option pricing. Appl. Math. Lett., 809-814 (2004) · Zbl 1063.65081
[19] Toivanen, J., Numerical valuation of European and American options under Kou’s jump-diffusion model. SIAM J. Sci. Comput., 1949-1970 (2008) · Zbl 1178.35225
[20] Boen, L.; in ’t Hout, K. J., Operator splitting schemes for American options under the two-asset merton jump-diffusion model. Appl. Numer. Math., 114-131 (2020) · Zbl 1444.91207
[21] Boen, L.; in ’t Hout, K. J., Operator splitting schemes for two-asset merton jump-diffusion model. J. Comput. Appl. Math. (2021)
[22] Arrarás, A.; in ’t Hout, K. J.; Hundsdorfer, W.; Portero, L., Modified douglas splitting methods for reaction-diffusion equations. BIT, 261-285 (2017) · Zbl 1367.65133
[23] Salmi, S.; Toivanen, J., An iterative method for pricing American options under jump-diffusion models. Appl. Numer. Math., 821-831 (2011) · Zbl 1213.91164
[24] Haghi, M.; Mollapourasl, R.; Vanmaele, M., An RBF-FD method for pricing American options under jump-diffusion models. Comput. Math. Appl., 2434-2459 (2018) · Zbl 1442.91100
[25] Mollapourasl, R.; Fereshtian, A.; Li, H.; Lu, X., RBF-PU method for pricing options under the jump-diffusion model with local volatility. J. Comput. Appl. Math., 98-118 (2018) · Zbl 1457.65148
[26] Mollapourasl, R.; Fereshtian, A.; Vanmaele, M., Radial basis functions with partition of unity method for American options with stochastic volatility. Comput. Econ., 259-287 (2019)
[27] Koleva, M. N.; Mudzimbabwe, W.; Vulkov, L. G., Fourth-order compact schemes for a parabolic-ordinary system of European option pricing liquidity shocks model. Numer. Algorithms, 59-75 (2017) · Zbl 1354.91166
[28] Kazmi, K., An IMEX predictor-corrector method for pricing options under regime-switching jump-diffusion models. Int. J. Comput. Math., 1137-1157 (2019) · Zbl 1481.91220
[29] Christara, C. C.; Leung, N. C.H., Option pricing in jump diffusion models with quadratic spline collocation. Appl. Math. Comput., 28-42 (2016) · Zbl 1410.91440
[30] Rao, S. C.S.; Manisha, Numerical solution of generalized black-scholes model. Appl. Math. Comput., 401-421 (2018) · Zbl 1427.91294
[31] Wilmott, P.; Dewynne, J.; Howison, S., Option Pricing: Mathematical Models and Computation (1998), Wiley
[32] Spijker, M., Stiffness in numerical initial-value problems. J. Comput. Appl. Math., 393-406 (1996) · Zbl 0857.65074
[33] Higham, D. J.; Terfethen, L. N., Stiffness of ODEs. BIT, 285-303 (1993) · Zbl 0782.65091
[34] Ascher, U. M.; Ruuth, S. J.; Spiteri, R. J., Implicit-explicit runge-kutta methods for time-dependent partial-differential equations. Appl. Numer. Math., 151-167 (1997) · Zbl 0896.65061
[35] Yadav, V. S.; Ganta, N.; Mahato, B.; Rajpoot, M. K.; Bhumkar, Y. G., New time-marching methods for compressible Navier-Stokes equations with applications to aeroacoustics problems. Appl. Math. Comput. (2022) · Zbl 1510.76117
[36] Yadav, V. S.; Singh, A.; Maurya, V.; Rajpoot, M. K., New RK type time-integration methods for stiff convection-diffusion-reaction systems. Comput. & Fluids (2023) · Zbl 1521.65065
[37] Pooley, D. M.; Vetzal, K. R.; Forsyth, P. A., Convergence remedies for non-smooth payoffs in option pricing. J. Comput. Finance, 25-40 (2003)
[38] Kadalbajoo, M. K.; Tripathi, L. P.; Kumar, A., Second order accurate IMEX methods for option pricing under merton and kou jump-diffusion models. J. Sci. Comput., 979-1024 (2015) · Zbl 1331.91191
[39] Lee, J.; Lee, Y., Stability of an implicit method to evaluate option prices under local volatility with jumps. Appl. Numer. Math., 20-30 (2015) · Zbl 1300.91052
[40] Wang, W.; Mao, M.; Wang, Z., An efficient variable step-size method for options pricing under jump-diffusion models with nonsmooth payoff function. ESAIM: M2AN, 913-938 (2021) · Zbl 1481.65160
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.