2015
DOI: 10.1017/s0021900200012298
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Optimal Dividend Policy when Cash Reserves Follow a Jump-Diffusion Process Under Markov-Regime Switching

Abstract: In this paper we study the optimal dividend payments for a company of limited liability whose cash reserves in the absence of dividends follow a Markov-modulated jumpdiffusion process with positive drifts and negative exponential jumps, where parameters and discount rates are modulated by a finite-state irreducible Markov chain. The main aim is to maximize the expected cumulative discounted dividend payments until bankruptcy time when cash reserves are nonpositive for the first time. We extend the results of J… Show more

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Cited by 5 publications
(6 citation statements)
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References 25 publications
(27 reference statements)
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“…While the literature on optimal stopping problems under regime switching is relatively rich (see, e.g., [4], [7], [16], [17], [35], among others), that on singular stochastic control problems with regime switching is still limited. We refer, e.g., to [25], [26], [32] and [37] where the optimal dividend problem of actuarial science is formulated as a one-dimensional problem under Markov regime switching. If we then further restrict our attention to singular stochastic control problems with a two-dimensional state space and regime shifts, to the best of our knowledge [18] is the only other paper available in the literature.…”
Section: Introductionmentioning
confidence: 99%
“…While the literature on optimal stopping problems under regime switching is relatively rich (see, e.g., [4], [7], [16], [17], [35], among others), that on singular stochastic control problems with regime switching is still limited. We refer, e.g., to [25], [26], [32] and [37] where the optimal dividend problem of actuarial science is formulated as a one-dimensional problem under Markov regime switching. If we then further restrict our attention to singular stochastic control problems with a two-dimensional state space and regime shifts, to the best of our knowledge [18] is the only other paper available in the literature.…”
Section: Introductionmentioning
confidence: 99%
“…In a discrete-time setting, the analysis is typically considered in the context of risk models for insurance companies (see, e.g., [42] and the more recent [41]). In continuous-time we find, e.g., [1] and [25] where the wealth process is a drifted Brownian motion and the interest rate is modulated by a continuous-time Markov chain (more recently [26] extends [25] to the case of a jumpdiffusive surplus process). Fixed-point methods are adopted in [25] and [26], whereas dynamic programming ideas appear in [1].…”
Section: Methodology and Resultsmentioning
confidence: 89%
“…In a discrete-time setting, the analysis is typically considered in the context of risk models for insurance companies (see, e.g., Xie and Zou (2010) and the more recent Tan et al (2015)). In continuous-time we find, for example, Akyildirim et al (2014) and Jiang and Pistorius (2012) where the wealth process is a drifted Brownian motion and the interest rate is modulated by a continuous-time Markov chain (more recently Jiang (2015) extends Jiang and Pistorius (2012) to the case of a jump-diffusive surplus process). Fixed-point methods are adopted in Jiang and Pistorius (2012) and Jiang (2015), whereas dynamic programing ideas appear in Akyildirim et al (2014).…”
Section: Related Literaturementioning
confidence: 92%