Abstract. We study the problem of optimal control of a jump diffusion, i.e. a process which is the solution of a stochastic differential equation driven by Lévy processes. It is required that the control process is adapted to a given subfiltration of the filtration generated by the underlying Lévy processes. We prove two maximum principles (one sufficient and one necessary) for this type of partial information control. The results are applied to a partial information mean-variance portfolio selection problem in finance.

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We consider a fully coupled forward backward stochastic differential equation driven by a Lévy processes having moments of all orders and an independent Brownian motion. Under some monotonicity assumptions, we prove the existence and uniqueness of solutions on an arbitrarily fixed large time duration. We use this result to prove the existence of an open-loop Nash equilibrium point for non-zero sum stochastic differential games.

We consider a control problem where the system is driven by a decoupled as well as a coupled forwardbackward stochastic differential equation. We prove the existence of an optimal control in the class of relaxed controls, which are measure-valued processes, generalizing the usual strict controls. The proof is based on some tightness properties and weak convergence on the space D of càdlàg functions, endowed with the Jakubowsky S-topology. Moreover, under some convexity assumptions, we show that the relaxed optimal control is realized by a strict control.

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