Moderate averaged deviations for a multi-scale system with jumps and memory

TL;DR

This paper establishes a moderate deviations principle for a two-time-scale jump-diffusion system with memory effects, using weak convergence and averaging techniques to analyze the small noise limit.

Contribution

It introduces a novel moderate deviations framework for multi-scale jump-diffusions with delay-dependent coefficients, extending existing theories to more complex systems.

Findings

Derived a moderate deviations principle for the slow component

Established an averaging principle for controlled processes

Utilized Khasminskii's technique for jump diffusions

Abstract

This work studies a two-time-scale functional system given by two jump-diffusions under the scale separation by a small parameter $\varepsilon \rightarrow 0$. The coefficients of the equations that govern the dynamics of the system depend on the segment process of the slow variable (responsible for capturing delay effects on the slow component) and on the state of the fast variable. We derive a moderate deviations principle for the slow component of the system in the small noise limit using the weak convergence approach. The rate function is written in terms of the averaged dynamics associated to the multi-scale system. The core of the proof of the moderate deviations principle is the establishment of an averaging principle for the controlled processes associated to the slow variable in the framework of the weak convergence approach. The controlled version of the averaging principle for jump multi-scale diffusions relies on the classical Khasminkii's technique.