Constrained quadratic risk minimization via forward and backward stochastic differential equations

Abstract

In this paper we study a continuous-time stochastic linear quadratic control problem arising from mathematical finance. We model the asset dynamics with random market coefficients and portfolio strategies with convex constraints. Following the convex duality approach, we show that the necessary and sufficient optimality conditions for both the primal and dual problems can be written in terms of processes satisfying a system of forward and backward stochastic differential equations (FBSDEs) together with other conditions. We characterize explicitly the optimal wealth and portfolio processes as functions of adjoint processes from the dual FBSDEs in a dynamic fashion, and vice versa. We apply the results to solve quadratic risk minimization problems with cone constraints and derive the explicit representations of solutions to the extended stochastic Riccati equations for such problems