Mean-Square Stability of Split-Step Theta Milstein Methods for Stochastic Differential Equations

Mahmoud A. Eissa, Haiying Zhang and Yu Xiao

Mathematical Problems in Engineering, 2018, vol. 2018, 1-13

Abstract:

The fundamental analysis of numerical methods for stochastic differential equations (SDEs) has been improved by constructing new split-step numerical methods. In this paper, we are interested in studying the mean-square (MS) stability of the new general drifting split-step theta Milstein (DSS M) methods for SDEs. First, we consider scalar linear SDEs. The stability function of the DSS M methods is investigated. Furthermore, the stability regions of the DSS M methods are compared with those of test equation, and it is proved that the methods with are stochastically A-stable. Second, the nonlinear stability of DSS M methods is studied. Under a coupled condition on the drifting and diffusion coefficients, it is proved that the methods with can preserve the MS stability of the SDEs with no restriction on the step-size. Finally, numerical examples are given to examine the accuracy of the proposed methods under the stability conditions in approximation of SDEs.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:1682513

DOI: 10.1155/2018/1682513

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