# Optimal approximation of anticipating SDEs

## TL;DR

This paper establishes the optimal convergence rate for approximating anticipating linear SDEs with Skorohod integrals, extending known results from Itô SDEs and developing new approximation techniques.

## Contribution

It introduces a generalized approach for optimal approximation of anticipating SDEs, extending existing methods for Wiener integrals to more complex random vectors.

## Key findings

- Optimal convergence rate derived for anticipating SDEs
- Extension of approximation results to correlated Wiener integrals
- Generalization from Itô to Skorohod SDEs

## Abstract

We derive the optimal rate of convergence for the mean squared error at the terminal point for anticipating linear stochastic differential equations, where the integral is interpreted in Skorohod sense. Although alternative proof techniques are needed, our results can be seen as generalizations of the corresponding results for It\=o SDEs. As a key tool we extend optimal approximation results for vectors of correlated Wiener integrals to general random vectors, which contain the solutions of our Skorohod SDEs.