Approximation of jump diffusions in finance and economics
Computational Economics
Strong convergence rates for backward Euler on a class of nonlinear jump-diffusion problems
Journal of Computational and Applied Mathematics
Strong approximations of stochastic differential equations with jumps
Journal of Computational and Applied Mathematics
The split-step backward Euler method for linear stochastic delay differential equations
Journal of Computational and Applied Mathematics
Compensated stochastic theta methods for stochastic differential equations with jumps
Applied Numerical Mathematics
Brief paper: The Wonham filter under uncertainty: A game-theoretic approach
Automatica (Journal of IFAC)
Physically consistent simulation of mesoscale chemical kinetics: The non-negative FIS-α method
Journal of Computational Physics
Runge-Kutta methods for jump-diffusion differential equations
Journal of Computational and Applied Mathematics
Applied Numerical Mathematics
Journal of Computational and Applied Mathematics
Mathematical and Computer Modelling: An International Journal
Asymptotic stability of balanced methods for stochastic jump-diffusion differential equations
Journal of Computational and Applied Mathematics
Journal of Computational and Applied Mathematics
Split-step θ-method for stochastic delay differential equations
Applied Numerical Mathematics
Hi-index | 0.01 |
We present and analyse two implicit methods for Ito stochastic differential equations (SDEs) with Poisson-driven jumps. The first method, SSBE, is a split-step extension of the backward Euler method. The second method, CSSBE, arises from the introduction of a compensated, martingale, form of the Poisson process. We show that both methods are amenable to rigorous analysis when a one-sided Lipschitz condition, rather than a more restrictive global Lipschitz condition, holds for the drift. Our analysis covers strong convergence and nonlinear stability. We prove that both methods give strong convergence when the drift coefficient is one-sided Lipschitz and the diffusion and jump coefficients are globally Lipschitz. On the way to proving these results, we show that a compensated form of the Euler–Maruyama method converges strongly when the SDE coefficients satisfy a local Lipschitz condition and the pth moment of the exact and numerical solution are bounded for some p2. Under our assumptions, both SSBE and CSSBE give well-defined, unique solutions for sufficiently small stepsizes, and SSBE has the advantage that the restriction is independent of the jump intensity. We also study the ability of the methods to reproduce exponential mean-square stability in the case where the drift has a negative one-sided Lipschitz constant. This work extends the deterministic nonlinear stability theory in numerical analysis. We find that SSBE preserves stability under a stepsize constraint that is independent of the initial data. CSSBE satisfies an even stronger condition, and gives a generalization of B-stability. Finally, we specialize to a linear test problem and show that CSSBE has a natural extension of deterministic A-stability. The difference in stability properties of the SSBE and CSSBE methods emphasizes that the addition of a jump term has a significant effect that cannot be deduced directly from the non-jump literature.