×

A collocation method for nonlinear stochastic differential equations driven by fractional Brownian motion and its application to mathematical finance. (English) Zbl 1540.65026

Summary: The main aim of this article is to demonstrate the collocation method based on the barycentric rational interpolation function to solve nonlinear stochastic differential equations driven by fractional Brownian motion. First of all, the corresponding integral form of the nonlinear stochastic differential equations driven by fractional Brownian motion is introduced. Then, collocation points followed by the Gauss-quadrature formula and Simpson’s quadrature method are used to reduce them into a system of algebraic equations. Finally, the approximate solution is obtained using Newton’s method. The rigorous convergence and error analysis of the presented method has been discussed in detail. The proposed method has been applied to some well-known stochastic models, such as the stock model and a few other examples, to demonstrate the applicability and plausibility of the discussed method. Also, the numerical results of the collocation method based on the barycentric rational interpolation function and barycentric Lagrange interpolation function get compared with an exact solution.

MSC:

65C30 Numerical solutions to stochastic differential and integral equations
60G22 Fractional processes, including fractional Brownian motion
65D05 Numerical interpolation
41A10 Approximation by polynomials
41A20 Approximation by rational functions
Full Text: DOI

References:

[1] Behera, S.; Saha Ray, S., Euler wavelets method for solving fractional-order linear Volterra-Fredholm integro-differential equations with weakly singular kernels, Comput Appl Math, 40, 1-30, 2021 · Zbl 1476.65335 · doi:10.1007/s40314-021-01565-9
[2] Biagini F, Hu Y, Øksendal B, Zhang T (2008) Stochastic calculus for fractional Brownian motion and applications. Springer Science & Business Media · Zbl 1157.60002
[3] Buckwar, E., Introduction to the numerical analysis of stochastic delay differential equations, J Comput Appl Math, 125, 1-2, 297-307, 2000 · Zbl 0971.65004 · doi:10.1016/S0377-0427(00)00475-1
[4] Canuto C, Hussaini MY, Quarteroni A, Zang TA (2007) Spectral methods: fundamentals in single domains. Springer Science & Business Media
[5] Diogo, T.; McKee, S.; Tang, T., A Hermite-type collocation method for the solution of an integral equation with a certain weakly singular kernel, IMA J Numer Anal, 11, 4, 595-605, 1991 · Zbl 0738.65096 · doi:10.1093/imanum/11.4.595
[6] Floater, MS; Hormann, K., Barycentric rational interpolation with no poles and high rates of approximation, Numer Math, 107, 315-331, 2007 · Zbl 1221.41002 · doi:10.1007/s00211-007-0093-y
[7] Guerra, J.; Nualart, D., Stochastic differential equations driven by fractional Brownian motion and standard Brownian motion, Stoch Anal Appl, 26, 5, 1053-1075, 2008 · Zbl 1151.60028 · doi:10.1080/07362990802286483
[8] Heydari, MH; Hooshmandasl, MR; Cattani, C.; Ghaini, FM, An efficient computational method for solving nonlinear stochastic Itô integral equations: Application for stochastic problems in physics, J Comput Phys, 283, 148-168, 2015 · Zbl 1351.60088 · doi:10.1016/j.jcp.2014.11.042
[9] Heydari, MH; Mahmoudi, MR; Shakiba, A.; Avazzadeh, Z., Chebyshev cardinal wavelets and their application in solving nonlinear stochastic differential equations with fractional Brownian motion, Commun Nonlinear Sci Numer Simul, 64, 98-121, 2018 · Zbl 1506.65020 · doi:10.1016/j.cnsns.2018.04.018
[10] Heydari, M.; Avazzadeh, Z.; Mahmoudi, M., Chebyshev cardinal wavelets for nonlinear stochastic differential equations driven with variable-order fractional Brownian motion, Chaos Solit Fractals, 124, 105-124, 2019 · Zbl 1448.60090 · doi:10.1016/j.chaos.2019.04.040
[11] Heydari, MH; Hooshmandasl, MR; Cattani, C., Wavelets method for solving nonlinear stochastic Itô-Volterra integral equations, Georgian Math J, 27, 1, 81-95, 2020 · Zbl 1457.60104 · doi:10.1515/gmj-2018-0009
[12] Liu, H.; Huang, J.; Pan, Y.; Zhang, J., Barycentric interpolation collocation methods for solving linear and nonlinear high-dimensional Fredholm integral equations, J Comput Appl Math, 327, 141-154, 2018 · Zbl 1372.65346 · doi:10.1016/j.cam.2017.06.004
[13] Maleknejad, K.; Derili, H., Numerical solution of integral equations by using combination of Spline-collocation method and Lagrange interpolation, Appl Math Comput, 175, 2, 1235-1244, 2006 · Zbl 1093.65125
[14] Mirzaee, F.; Alipour, S., An efficient cubic B-spline and bicubic B-spline collocation method for numerical solutions of multidimensional nonlinear stochastic quadratic integral equations, Math Methods Appl Sci, 43, 1, 384-397, 2020 · Zbl 1452.65019 · doi:10.1002/mma.5890
[15] Mirzaee, F.; Samadyar, N., Numerical solution of nonlinear stochastic Itô-Volterra integral equations driven by fractional Brownian motion, Math Methods Appl Sci, 41, 4, 1410-1423, 2018 · Zbl 1390.60253 · doi:10.1002/mma.4671
[16] Samadyar, N.; Ordokhani, Y.; Mirzaee, F., Hybrid Taylor and block-pulse functions operational matrix algorithm and its application to obtain the approximate solution of stochastic evolution equation driven by fractional Brownian motion, Commun Nonlinear Sci Numer Simul, 90, 2020 · Zbl 07265405 · doi:10.1016/j.cnsns.2020.105346
[17] Singh, PK; Ray, SS, An efficient numerical method based on Lucas polynomials to solve multi-dimensional stochastic Itô-Volterra integral equations, Math Comput Simul, 203, 826-845, 2023 · Zbl 1540.65568 · doi:10.1016/j.matcom.2022.06.029
[18] Singh, PK; Saha Ray, S., Shifted Chebyshev spectral Galerkin method to solve stochastic Itô-Volterra integral equations driven by fractional Brownian motion appearing in mathematical physics, Comput Appl Math, 42, 3, 120, 2023 · Zbl 1538.65623 · doi:10.1007/s40314-023-02263-4
[19] Wen, X.; Huang, J., A combination method for numerical solution of the nonlinear stochastic Itô-Volterra integral equation, Appl Math Comput, 407, 10, 2021 · Zbl 1510.65020
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.