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This work focuses on numerical methods for finding optimal dividend payment and capital injection policies to maximize the present value of the difference between the cumulative dividend payment and the possible capital injections.Using dynamic programming principle,the value function obeys a quasi-variational inequality(QVI).The state constraint of the impulsive control gives rise to a capital injection region with free boundary.Since the closed-form solutions are virtually impossible to obtain,we use Markov chain approximation techniques to construct a discrete-time controlled Markov chain to approximate the value function and optimal controls.Convergence of the approximation algorithms is proved.
This work focuses on numerical methods for finding optimal dividend payment and capital injection policies to maximize the present value of the difference between the cumulative dividend payment and the possible capital injections. Using dynamic programming principle, the value function obeys a quasi-variational inequality (QVI ). The state constraint of the impulsive control gives rise to a capital injection region with free boundary. Since the closed-form solutions are virtually impossible to obtain, we use Markov chain approximation techniques to construct a discrete-time controlled Markov chain to approximate the value function and optimal controls. Convergence of the approximation algorithms is proved.