This paper develops a sufficient stochastic maximum principle for a stochastic optimal control problem, where the state process is governed by a continuous-time Markov regime-switching jump-diffusion model. We also establish the relationship between the stochastic maximum principle and the dynamic programming principle in a Markovian case. Applications of the stochastic maximum principle to the mean-variance portfolio selection problem are discussed.

In this paper, we develop a global form stochastic maximum principle for a Markov regime switching mean-field model driven by Brownian motions and Poisson jumps. The form of the maximum principle turns out to be quite different from the classical one in the sense that here the first order adjoint process is defined as a solution to a certain jump-diffusion mean-field backward stochastic differential equation (BSDE), but the second order adjoint equation is still a classical BSDE. As an application, we apply the main results to study a linear quadratic stochastic control problem of mean-field type.