Compensated stochastic theta methods for stochastic differential equations with jumps
Applied Numerical Mathematics
Asymptotic stability of balanced methods for stochastic jump-diffusion differential equations
Journal of Computational and Applied Mathematics
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Asymptotic linear stability is studied for stochastic differential equations (SDEs) that incorporate Poisson-driven jumps and their numerical simulations using theta-method discretizations. The property is shown to have a simple explicit characterization for the SDE, whereas for the discretization a condition is found that is amenable to numerical evaluation. This allows us to evaluate the asymptotic stability behavior of the methods. One surprising observation is that there exist problem parameters for which an explicit, forward Euler method has better stability properties than its trapezoidal and backward Euler counterparts. Other computational experiments indicate that all theta methods reproduce the correct asymptotic linear stability for sufficiently small step sizes. By using a recent result of Appleby, Berkolaiko, and Rodkina, we give a rigorous verification that both linear stability and instability are reproduced for small step sizes. This property is known not to hold for general, nonlinear problems.