Existence, minimality and approximation of solutions to BSDEs with convex drivers

TL;DR

This paper investigates the existence, minimality, and approximation of solutions to backward stochastic differential equations with convex drivers, focusing on cases with Lipschitz and continuous terminal conditions, and constructing solutions via approximation methods.

Contribution

It establishes existence and minimality results for solutions to BSDEs with convex drivers without growth restrictions, using approximation techniques for various terminal conditions.

Findings

Existence of unique solutions with bounded Z for Lipschitz terminal conditions.

Construction of minimal supersolutions via approximation of terminal conditions.

Solutions can be approximated by discretized equations and pointwise limits.

Abstract

We study the existence of solutions to backward stochastic differential equations with drivers f(t,W,y,z) that are convex in z. We assume f to be Lipschitz in y and W but do not make growth assumptions with respect to z. We first show the existence of a unique solution (Y,Z) with bounded Z if the terminal condition is Lipschitz in W and that it can be approximated by the solutions to properly discretized equations. If the terminal condition is bounded and uniformly continuous in W, we show the existence of a minimal continuous supersolution by uniformly approximating the terminal condition with Lipschitz terminal conditions. Finally, we prove existence of a minimal RCLL supersolution for bounded lower semicontinuous terminal conditions by approximating the terminal condition pointwise from below with Lipschitz terminal conditions.