Mean-Square and Asymptotic Stability of the Stochastic Theta Method
SIAM Journal on Numerical Analysis
Strong Convergence of Euler-Type Methods for Nonlinear Stochastic Differential Equations
SIAM Journal on Numerical Analysis
Simulation of stopped diffusions
Journal of Computational Physics
Journal of Computational and Applied Mathematics
SIAM Journal on Numerical Analysis
Multilevel Monte Carlo Path Simulation
Operations Research
Journal of Computational and Applied Mathematics
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We are interested in the strong convergence and almost sure stability of Euler-Maruyama (EM) type approximations to the solutions of stochastic differential equations (SDEs) with non-linear and non-Lipschitzian coefficients. Motivation comes from finance and biology where many widely applied models do not satisfy the standard assumptions required for the strong convergence. In addition we examine the globally almost surely asymptotic stability in this non-linear setting for EM type schemes. In particular, we present a stochastic counterpart of the discrete LaSalle principle from which we deduce stability properties for numerical methods.