In this article we compare the mean-square stability properties of the ?-Maruyama and ?-Milstein method that are used to solve stochastic differential equations. For the linear stability analysis, we propose an extension of the standard geometric Brownian motion as a test equation and consider a scalar linear test equation with several multiplicative noise terms. This test equation allows to begin investigating the influence of multi-dimensional noise on the stability behaviour of the methods while the analysis is still tractable. Our findings include: (i) the stability condition for the ?-Milstein method and thus, for some choices of ?, the conditions on the step-size, are much more restrictive than those for the ?-Maruyama method; (ii) the precise stability region of the ?-Milstein method explicitly depends on the noise terms. Further, we investigate the effect of introducing partial implicitness in the diffusion approximation terms of Milstein-type methods, thus obtaining the possibility to control the stability properties of these methods with a further method parameter s. Numerical examples illustrate the results and provide a comparison of the stability behaviour of the different methods. © 2010 IMACS. Published by Elsevier B.V. All rights reserved.
- θ-Maruyama method
- θ-Milstein method
- Asymptotic mean-square stability
- Linear stability analysis
- Stochastic differential equations