Distribution dependent SDEs with multiplicative fractional noise

TL;DR

This paper investigates the well-posedness and deviation principles for distribution dependent stochastic differential equations driven by fractional Brownian motion with multiplicative noise, using a novel H"older space framework.

Contribution

It introduces a new H"older space of probability measure paths and extends large and moderate deviation principles to fractional Brownian motion driven SDEs with multiplicative noise.

Findings

Established well-posedness using contraction mapping and fractional calculus.

Extended deviation principles to fractional Brownian motion setting.

Developed a complete metric space framework for measure paths.

Abstract

The well-posedness is investigated for distribution dependent stochastic differential equations driven by fractional Brownian motion with Hurst parameter $H\in (\ff {\sq 5-1} 2,1)$ and distribution dependent multiplicative noise. To this aim, we introduce a H\"older space of probability measure paths which is a complete metric space under a new metric. Our arguments rely on a mix of contraction mapping principle on the H\"older space and fractional calculus tools. We also establish the large and moderate deviation principles for this type of equations via the weak convergence criteria in the factional Brownian motion setting, which extend previously known results in the additive setting.