Stability Properties of Systems of Linear Stochastic Differential Equations with Random Coefficients

TL;DR

This paper investigates the stability of linear stochastic differential equations with random coefficients, providing new perturbation conditions and estimates that do not rely on Lyapunov functions or ergodic properties.

Contribution

It introduces novel weak perturbation conditions and log-Lyapunov estimates for stability analysis of SDEs with random coefficients, without using traditional Lyapunov or ergodic methods.

Findings

Established new stability criteria for random linear SDEs.

Derived exponential contraction inequalities under small fluctuation parameters.

First results of their kind for this class of equations.

Abstract

This work is concerned with the stability properties of linear stochastic differential equations with random (drift and diffusion) coefficient matrices, and the stability of a corresponding random transition matrix (or exponential semigroup). We consider a class of random matrix drift coefficients that involves random perturbations of an exponentially stable flow of deterministic (time-varying) drift matrices. In contrast with more conventional studies, our analysis is not based on the existence of Lyapunov functions, and it does not rely on any ergodic properties. These approaches are often difficult to apply in practice when the drift/diffusion coefficients are random. We present rather weak and easily checked perturbation-type conditions for the asymptotic stability of time-varying and random linear stochastic differential equations. We provide new log-Lyapunov estimates and exponential contraction inequalities on any time horizon as soon as the fluctuation parameter is sufficiently small. These seem to be the first results of this type for this class of linear stochastic differential equations with random coefficient matrices.