White-noise driven conditional McKean-Vlasov limits for systems of particles with simultaneous and random jumps

TL;DR

This paper investigates the convergence of particle systems influenced by Brownian motion and Poisson jumps, revealing that the limit describes a conditional distribution with dependencies, leading to a non-linear SDE with random noise.

Contribution

It introduces the concept of conditional McKean-Vlasov limits for particle systems with simultaneous jumps and random scaling, extending the understanding of propagation of chaos.

Findings

Limit empirical measure is random and describes a conditional distribution.

The limit system is a non-linear SDE with dependent martingale measures.

Conditional propagation of chaos is established in the model.

Abstract

We study the convergence of $N-$particle systems described by SDEs driven by Brownian motion and Poisson random measure, where the coefficients depend on the empirical measure of the system. Every particle jumps with a jump rate depending on its position and on the empirical measure of the system. Jumps are simultaneous, that is, at each jump time, all particles of the system are affected by this jump and receive a random jump height that is centred and scaled in $N^{-1/2}.$ This particular scaling implies that the limit of the empirical measures of the system is random, describing the conditional distribution of one particle in the limit system. We call such limits {\it conditional McKean-Vlasov limits}. The conditioning in the limit measure reflects the dependencies between coexisting particles in the limit system such that we are dealing with a {\it conditional propagation of chaos property}. As a consequence of the scaling in $N^{-1/2}$ and of the fact that the limit of the empirical measures is not deterministic the limit system turns out to be solution of a non-linear SDE, where not independent martingale measures and white noises appear having an intensity that depends on the conditional law of the process.