Approximation of continuous time stochastic processes by a local linearization method
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- Math. Comp. 67 (1998), 287-298 Request permission
Abstract:
This paper investigates the rate of convergence of an alternative approximation method for stochastic differential equations. The rates of convergence of the one-step and multi-step approximation errors are proved to be $O((\Delta t)^2)$ and $O(\Delta t)$ in the $L_p$ sense respectively, where $\Delta t$ is discrete time interval. The rate of convergence of the one-step approximation error is improved as compared with methods assuming the value of Brownian motion to be known only at discrete time. Through numerical experiments, the rate of convergence of the multi-step approximation error is seen to be much faster than in the conventional method.References
- Chien Cheng Chang, Numerical solution of stochastic differential equations with constant diffusion coefficients, Math. Comp. 49 (1987), no. 180, 523–542. MR 906186, DOI 10.1090/S0025-5718-1987-0906186-6
- J. M. C. Clark and R. J. Cameron, The maximum rate of convergence of discrete approximations for stochastic differential equations, Stochastic differential systems (Proc. IFIP-WG 7/1 Working Conf., Vilnius, 1978) Lecture Notes in Control and Information Sci., vol. 25, Springer, Berlin-New York, 1980, pp. 162–171. MR 609181
- Ioannis Karatzas and Steven E. Shreve, Brownian motion and stochastic calculus, 2nd ed., Graduate Texts in Mathematics, vol. 113, Springer-Verlag, New York, 1991. MR 1121940, DOI 10.1007/978-1-4612-0949-2
- Yu. A. Kutoyants, Parameter estimation for stochastic processes, Research and Exposition in Mathematics, vol. 6, Heldermann Verlag, Berlin, 1984. Translated from the Russian and edited by B. L. S. Prakasa Rao. MR 777685
- G. N. Mil′šteĭn, Approximate integration of stochastic differential equations, Teor. Verojatnost. i Primenen. 19 (1974), 583–588 (Russian, with English summary). MR 0356225
- G. N. Mil′šteĭn, A method with second order accuracy for the integration of stochastic differential equations, Teor. Verojatnost. i Primenen. 23 (1978), no. 2, 414–419 (Russian, with English summary). MR 0517998
- Nigel J. Newton, Asymptotically efficient Runge-Kutta methods for a class of Itô and Stratonovich equations, SIAM J. Appl. Math. 51 (1991), no. 2, 542–567. MR 1095034, DOI 10.1137/0151028
- Ozaki, T., Statistical identification of storage models with application to stochastic hydrology, Water Resources Bulletin 21 (1985), 663–675.
- W. Rümelin, Numerical treatment of stochastic differential equations, SIAM J. Numer. Anal. 19 (1982), no. 3, 604–613. MR 656474, DOI 10.1137/0719041
- Shoji, I. and Ozaki, T., Estimation for nonlinear stochastic differential equations by a local linearization method, forthcoming in Stochastic Analysis and Applications.
- Nakahiro Yoshida, Estimation for diffusion processes from discrete observation, J. Multivariate Anal. 41 (1992), no. 2, 220–242. MR 1172898, DOI 10.1016/0047-259X(92)90068-Q
Additional Information
- Isao Shoji
- Affiliation: Institute of Policy and Planning Sciences, University of Tsukuba, Tsukuba Ibaraki 305, Japan
- Email: shoji@shako.sk.tsukuba.ac.jp
- Received by editor(s): May 19, 1996
- Received by editor(s) in revised form: September 4, 1996
- © Copyright 1998 American Mathematical Society
- Journal: Math. Comp. 67 (1998), 287-298
- MSC (1991): Primary 65D30, 65B33; Secondary 60H10
- DOI: https://doi.org/10.1090/S0025-5718-98-00888-6
- MathSciNet review: 1432134