BS\Delta Es and BSDEs with non-Lipschitz drivers: Comparison, convergence and robustness

TL;DR

This paper establishes existence, comparison, and convergence results for non-Lipschitz backward stochastic difference equations (BS$ abla$Es) and their convergence to backward stochastic differential equations (BSDEs), including robustness under approximation.

Contribution

It introduces new existence and comparison principles for non-Lipschitz BS$ abla$Es and proves their convergence to BSDEs, extending the theory to non-Lipschitz drivers and convex cases.

Findings

Convergence of BS$ abla$Es to BSDE solutions under non-Lipschitz drivers.

Existence and comparison principles for non-Lipschitz BS$ abla$Es.

Robustness of BSDE solutions under approximation of drivers and underlying processes.

Abstract

We provide existence results and comparison principles for solutions of backward stochastic difference equations (BS$\Delta$Es) and then prove convergence of these to solutions of backward stochastic differential equations (BSDEs) when the mesh size of the time-discretizaton goes to zero. The BS$\Delta$Es and BSDEs are governed by drivers $f^N(t,\omega,y,z)$ and $f(t,\omega,y,z),$ respectively. The new feature of this paper is that they may be non-Lipschitz in z. For the convergence results it is assumed that the BS$\Delta$Es are based on d-dimensional random walks $W^N$ approximating the d-dimensional Brownian motion W underlying the BSDE and that $f^N$ converges to f. Conditions are given under which for any bounded terminal condition $\xi$ for the BSDE, there exist bounded terminal conditions $\xi^N$ for the sequence of BS$\Delta$Es converging to $\xi$, such that the corresponding solutions converge to the solution of the limiting BSDE. An important special case is when $f^N$ and f are convex in z. We show that in this situation, the solutions of the BS$\Delta$Es converge to the solution of the BSDE for every uniformly bounded sequence $\xi^N$ converging to $\xi$. As a consequence, one obtains that the BSDE is robust in the sense that if $(W^N,\xi^N)$ is close to $(W,\xi)$ in distribution, then the solution of the Nth BS$\Delta$E is close to the solution of the BSDE in distribution too.