Asymptotic version of the parametrix method for Markov chains converging to diffusions

TL;DR

This paper extends the local limit theorem to inhomogeneous Markov chains with weak regularity, showing their convergence to diffusion processes even with unbounded drift coefficients, using parametrix methods.

Contribution

It generalizes the parametrix method for Markov chains converging to diffusions under weak regularity and unbounded coefficients, providing new convergence rate estimates.

Findings

Established convergence of inhomogeneous Markov chains to diffusions with unbounded drift.

Derived explicit estimates for the transfer of terminal states through deterministic flows.

Provided a generalized local limit theorem applicable under weak regularity conditions.

Abstract

The paper presents a generalization of the local limit theorem on the convergence of inhomogeneous Markov chains to the diffusion limit for the case where the corresponding process coefficients satisfy weak regularity conditions and coincide only asymptotically. In particular, the drift coefficients considered by us can be unbounded with at most linear growth, and the estimates reflect the transfer of the terminal state by an unbounded trend through the corresponding deterministic flow. Our approach is based on the study of the uniform distance between the transition densities of a given inhomogeneous Markov chain and the limit diffusion process, and the convergence rate estimate is obtained using the classical local limit theorem and parametrix-type stability estimates.