A weak Local Linearization scheme for stochastic differential equations with multiplicative noise

TL;DR

This paper introduces a weak Local Linearization numerical scheme for stochastic differential equations with multiplicative noise, providing convergence analysis and demonstrating its effectiveness through numerical simulations.

Contribution

It presents a novel weak Local Linearization scheme for SDEs with multiplicative noise, including convergence rate analysis and numerical validation.

Findings

The scheme achieves a specific convergence rate.

Numerical simulations confirm the scheme's accuracy.

The method effectively approximates solutions of SDEs with multiplicative noise.

Abstract

In this paper, a weak Local Linearization scheme for Stochastic Differential Equations (SDEs) with multiplicative noise is introduced. First, for a time discretization, the solution of the SDE is locally approximated by the solution of the piecewise linear SDE that results from the Local Linearization strategy. The weak numerical scheme is then defined as a sequence of random vectors whose first moments coincide with those of the piecewise linear SDE on the time discretization. The rate of convergence is derived and numerical simulations are presented for illustrating the performance of the scheme.