Optimal Control of Predictive Mean-Field Equations and Applications to Finance

We study a coupled system of controlled stochastic differential equations (SDEs) driven by a Brownian motion and a compensated Poisson random measure, consisting of a forward SDE in the unknown process X(t) and a predictivemean- field backward SDE (BSDE) in the unknowns Y (t), Z(t), K(t, .). The driver of the BSDE at time t may depend not just upon the unknown processes Y (t), Z(t), K(t, .), but also on the predicted future value Y (t + delta), defined by the conditional expectation A(t) := E[Y (t + delta)| F-t]. We give a sufficient and a necessary maximum principle for the optimal control of such systems, and then we apply these results to the following two problems: (i) Optimal portfolio in a financial market with an insider influenced asset price process. (ii) Optimal consumption rate from a cash flow modeled as a geometric Ito-Levy SDE, with respect to predictive recursive utility.