TWO-STEP ORDER 3/2 STRONG METHOD FOR APPROXIMATING STOCHASTIC DIFFERENTIAL EQUATIONS
Abstract
In this paper, we consider two-step order strong scheme for getting numerical solutions of stochastic differential equations (SDEs) of order 3/2. It follows a new technique based on replacing stochastic integrals $I_\alpha$ by random variables. Thus we do not need to calculate $I_\alpha$. We employ Itô-Taylor expansion and Runge-Kutta method to get the approximate solutions of the desired order. The experimental results of the approximation method and its error are provided to confirm the validity of the method.
References
[1]A. Alfonsi, B. Jourdain and A. Kohatsu-Higa, Pathwise optimal transport bounds between a one-dimensional diffusion and its Euler scheme, Ann. Appl. Probab. 24(3) (2014), 1049-1080.
[2]A. Alfonsi, B. Jourdain and A. Kohatsu-Higa, Optimal transport bounds between the time-marginals of a multidimensional diffusion and its Euler scheme, arXiv:1405.7007, 19-Mar-2015. [Online]. Available: https://arxiv.org/abs/1405.7007v2 [Accessed: 19-Mar-2015].
[3]C. J. S. Alves and A. B. Cruzeiro, Monte Carlo simulation of stochastic differential systems - a geometrical approach, Stochastic Processes Appl. 118(3) (2008), 346-367.
[4]A. B. Cruzeiro and P. Malliavin, Numerical approximation of diffusions in using normal charts of a Riemannian manifold, Stochastic Processes Appl. 116(7) (2006), 1088-1095.
[5]A. Davie, KMT theory applied to approximations of SDE, Springer Proceeding in Mathematics and Statistics 100 (2014), 185-201.
[6]A. M. Davie, Pathwise approximation of stochastic differential equations using coupling, Preprint, 28-May-2015. [Online] Available: http://www.maths.ed.ac.uk/~sandy/-coum.pdf [Accessed: 28-May-2015].
[7]A. M. Davie, Polynomial perturbations of normal distributions, Preprint, 25-Sep-2017. [Online]. Available: https://www.maths.ed.ac.uk/sandy/polg.pdf [Accessed: 25-Sep-2017].
[8]A. Deya, A. Neuenkirch and S. Tindel, A Milstein-type scheme without Levy area terms for SDEs driven by fractional Brownian motion, Ann. Inst. H. Poincare Probab. Statist. 48(2) (2010), 518-550.
[9]M. Gelbrich, Simultaneous time and chance discretization for stochastic differential equations, J. Comput. Appl. Math. 58(3) (1995), 255-289.
[10]C. Villani, Topics in Optimal Transportation, American Mathematical Society, London, 2003.
[11]J. G. Gaines and T. J. Lyons, Random generation of stochastic area integrals, SIAM J. Appl. Math. 54(4) (1994), 1132-1146.
[12]P. E. Kloeden and E. Platen, Numerical Solution of Stochastic Differential Equations, Springer-Verlag, Berlin, 1995.
[13]M. Wiktorsson, Joint characteristic function and simultaneous simulation of iterated Itô integrals for multiple independent Brownian motions, Ann. Appl. Probab. 11(1) (2001), 470-487.
[14]T. Rydén and M. Wiktorsson, On the simulation of iterated Ito integrals, Stochastic Processes Appl. 91(1) (2001), 151-168.