Numerical stability revisited: A family of Benchmark problems for the analysis of explicit stochastic differential equation integrators

Abstract

We revisit the numerical stability of four well-established explicit stochastic integration schemes through a new generic benchmark stochastic differential equation (SDE) designed to assess asymptotic statistical accuracy and stability properties. This one-parameter benchmark equation is derived from a general one-dimensional first-order SDE using spatio-temporal nondimensionalization and is employed to evaluate the performance of the (1) Euler–Maruyama, (2) Milstein, (3) Stochastic Heun, and (4) three-stage Runge–Kutta schemes. Our findings reveal that lower-order schemes can outperform higher-order ones over a range of time-step sizes, depending on the benchmark parameters and application context. The theoretical results are validated through a series of numerical experiments, and we discuss their implications for more general applications, including a nonlinear example. Our results suggest that the insights obtained from the linear benchmark problem provide reliable guidance for time-stepping strategies when simulating nonlinear SDEs.