# Donsker-Type Theorem for BSDEs: Rate of Convergence

## TL;DR

This paper investigates the convergence rate of a Markovian backward stochastic differential equation (BSDE) approximation driven by a scaled random walk, extending Donsker-type theorems to BSDEs and analyzing their Wasserstein distance convergence.

## Contribution

It introduces a Donsker-type theorem for BSDEs, providing a quantitative rate of convergence for approximations driven by scaled random walks.

## Key findings

- Establishes a convergence rate in Wasserstein distance for BSDE approximations.
- Extends classical Donsker theorems to the context of BSDEs.
- Provides theoretical bounds for approximation accuracy.

## Abstract

In this paper, we study in the Markovian case the rate of convergence in the Wasserstein distance of an approximation of the solution to a BSDE given by a BSDE which is driven by a scaled random walk as introduced in Briand, Delyon and M{\'e}min (Electron. Comm. Probab. 6(2001),1-14).