Dividend maximization in a hidden Markov switching model

TL;DR

This paper addresses the problem of optimizing dividend payouts for an insurance company operating in a changing economic environment modeled by a hidden Markov process, using filtering theory to handle partial information.

Contribution

It introduces an analytic framework for dividend maximization under a hidden Markov switching model with partial information, providing a novel approach to valuation and payout strategies.

Findings

Optimal dividend strategies depend on the economic state.

Filtering techniques effectively estimate unobservable economic states.

Numerical results illustrate payout strategies across scenarios.

Abstract

In this paper we study the valuation problem of an insurance company by maximizing the expected discounted future dividend payments in a model with partial information that allows for a changing economic environment. The surplus process is modeled as a Brownian motion with drift. This drift depends on an underlying Markov chain the current state of which is assumed to be unobservable. The different states of the Markov chain thereby represent different phases of the economy. We apply results from filtering theory to overcome uncertainty and then we give an analytic characterization of the optimal value function. Finally, we present a numerical study covering various scenarios to get a clear picture of how dividends should be paid out.