Adaptive Euler methods for stochastic systems with non-globally Lipschitz coefficients
C\'onall Kelly, Gabriel Lord

TL;DR
This paper introduces adaptive explicit and semi-implicit Euler schemes for stiff stochastic differential equations with non-globally Lipschitz coefficients, ensuring strong convergence and demonstrating numerical robustness and efficiency.
Contribution
It develops adaptive schemes that adapt stepsize based on drift, achieving strong convergence for non-globally Lipschitz SDEs, with practical numerical comparisons showing improved robustness and efficiency.
Findings
Strong convergence with order $(1- ext{epsilon})/2$ proven
Adaptive semi-implicit method outperforms explicit and fully implicit methods
Numerical experiments confirm robustness and efficiency of the proposed schemes
Abstract
We present strongly convergent explicit and semi-implicit adaptive numerical schemes for systems of stiff stochastic differential equations (SDEs) where both the drift and diffusion are non-globally Lipschitz continuous. This stiffness may originate either from a linear operator in the drift, or from a perturbation of the nonlinear structures under discretisation, or both. Typical applications arise from the space discretisation of an SPDE, stochastic volatility models in finance, or certain ecological models. We prove that a timetepping strategy that adapts the stepsize based on the drift alone is sufficient to control growth and to obtain strong convergence with polynomial order. The order of strong convergence of our scheme is , for , where becomes arbitrarily small as the number of available finite moments for solutions of the…
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