Document details for 'Convergence of the Euler scheme for a class of stochastic differential'

Authors Marion, G., Mao, X. and Renshaw, E.
Publication details International Mathematical Journal 1, 9-22.
Keywords stochastic differential equations
Abstract Stochastic differential equations provide a useful means of introducing stochasticity into models across a broad range of systems from chemistry to population biology. However, in many applications the resulting equations have so far proved intractable to direct analytical solution. Numerical approximations, such as the Euler scheme are therefore a vital tool in exploring model behaviour. Unfortunately, current results concerning the convergence of such schemes impose conditions on the drift and diffusion coefficients of the stochastic differential equation, namely the linear growth and global Lipschitz conditions, which are often not met by systems of interest. In this paper we relax these conditions and prove that numerical solutions based on the Euler scheme will converge to the true solution of a broad class of stochastic differential equations. The results are illustrated by application to a stochastic Lotka-Volterra model and a model of chemical auto-catalysis, neither of which satisfy either the linear growth nor the global Lipschitz conditions.
Last updated 2004-06-07
Files
  1. paper.ps.gz
  2. paper.pdf

Unless explicitly stated otherwise, all material is copyright © Biomathematics and Statistics Scotland.

Biomathematics and Statistics Scotland (BioSS) is formally part of The James Hutton Institute (JHI), a registered Scottish charity No. SC041796 and a company limited by guarantee No. SC374831. Registered Office: JHI, Invergowrie, Dundee, DD2 5DA, Scotland