Approximation of optimal ergodic dividend strategies using controlled Markov chains

This study develops a numerical method to find optimal ergodic (long-run average) dividend strategies in a regimeswitching model. The surplus process is modelled by a regime-switching process subject to liability constraints. The regimeswitching process is modelled by a finite-time continuous-time Markov chain. Using the dynamic programming principle, the
optimal long-term average dividend payment is a solution to the coupled system of Hamilton–Jacobi–Bellman equations. Under
suitable conditions, the optimal value of the long-term average dividend payment can be determined by using an invariant
measure. However, due to the regime switching, getting the invariant measure is very difficult. The objective is to design a
numerical algorithm to approximate the optimal ergodic dividend payment strategy. By using the Markov chain approximation
techniques, the authors construct a discrete-time controlled Markov chain for the approximation, and prove the convergence of
the approximating sequences. A numerical example is presented to demonstrate the applicability of the algorithm.