Conditional Expectation Backward Stochastic Differential Equations and Related Backward Stochastic Differential Equations with Conditional Reflection
Hanwu Li
TL;DR
This work introduces conditional expectation BSDEs in which the driver depends on conditional expectations given a subfiltration, unifying classical BSDEs and mean-field BSDEs. It develops well-posedness results (existence, uniqueness) and a comparison framework under mild Lipschitz conditions and extra structural assumptions, without requiring standard conditions on the subfiltration. The authors also provide a penalization-based construction for conditional reflected BSDEs that does not assume continuity of the subfiltration, proving convergence of penalized solutions to the reflected solution and recovering classical special cases in degenerate information setups. The results offer a robust, information-structure-aware extension of BSDE theory with potential partial-information applications and numerical appeal for conditional reflection problems.
Abstract
In this paper, we introduce a new type of backward stochastic differential equations (BSDEs), called conditional expectation BSDEs, whose drivers depend not only on the value of the solutions but also on their conditional expectations with respect to a certain sub-σ-algebra. The collection of these sub-σ-algebra forms a subfiltration, which stands for partial information that is common for decision making applications. The classical BSDEs and the mean-field BSDEs can be regarded as two special and extreme cases of conditional expectation BSDEs. We establish the well-posedness for conditional expectation BSDEs under mild conditions and discuss the comparison results. Then, we provide an alternative construction for the solutions to conditional reflected BSDEs without the continuity assumption for the subfiltration, which can be seen as the limit of a sequence of penalized conditional expectation BSDEs.