Backward stochastic differential equations with non-Markovian singular terminal conditions for general driver and filtration

Abstract

We consider a class of Backward Stochastic Differential Equations with superlinear driver process f adapted to a filtration supporting at least a d dimensional Brownian motion and a Poisson random measure on ${\mathbb{R}}^m\setminus \{0\}$. We consider the following class of terminal conditions: ${\mathrm{\xi }}_1=\mathrm{\infty }\cdot {\mathbf{1}}_{\{{\mathit{\tau }}_1\le T\}}$ where ${\mathit{\tau }}_1$ is any stopping time with a bounded density in a neighborhood of T and ${\mathrm{\xi }}_2=\mathrm{\infty }\cdot {\mathbf{1}}_{A_T}$ where $A_t$, $t\in [0,T]$ is a decreasing sequence of events adapted to the filtration ${\mathcal{F}}_t$ that is continuous in probability at T (equivalently, $A_T=\{{\mathit{\tau }}_2>T\}$ where ${\mathit{\tau }}_2$ is any stopping time such that $\mathbb{P}({\mathit{\tau }}_2=T)=0$). In this setting we prove that the minimal supersolutions of the BSDE are in fact solutions, i.e., they attain almost surely their terminal values. We note that the first exit time from a time varying domain of a d-dimensional diffusion process driven by the Brownian motion with strongly elliptic covariance matrix does have a continuous density. Therefore such exit times can be used as ${\mathit{\tau }}_1$ and ${\mathit{\tau }}_2$ to define the terminal conditions ${\mathrm{\xi }}_1$ and ${\mathrm{\xi }}_2$. The proof of existence of the density is based on the classical Green’s functions for the associated PDE.