Stationary distribution of the stochastic theta method for nonlinear stochastic differential equations
Provides theoretical guarantees for long-term numerical simulation of stochastic differential equations, relevant for researchers in computational stochastics.
The paper proves existence and uniqueness of the stationary distribution for the stochastic theta method applied to nonlinear SDEs, and shows convergence of the numerical stationary distribution to the true one under different parameter conditions.
The existence and uniqueness of the stationary distribution of the numerical solution generated by the stochastic theta method is studied. When the parameter theta takes different values, the requirements on the drift and diffusion coefficients are different. The convergence of the numerical stationary distribution to the true counterpart is investigated. Several numerical experiments are presented to demonstrate the theoretical results.