Dividend maximization in a hidden Markov switching model

IF 1.3 Q2 STATISTICS & PROBABILITY Statistics & Risk Modeling Pub Date : 2016-02-15 DOI:10.1515/strm-2015-0019
Michaela Szolgyenyi
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引用次数: 7

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.
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隐马尔可夫切换模型中的红利最大化
本文在考虑经济环境变化的部分信息模型中,研究了保险公司的估值问题。剩余过程被建模为带漂移的布朗运动。这种漂移依赖于一个潜在的马尔可夫链,该链的当前状态被认为是不可观察的。因此,马尔可夫链的不同状态代表了经济的不同阶段。利用滤波理论的结果克服了不确定性,给出了最优值函数的解析表征。最后,我们提出了一个涵盖各种场景的数值研究,以清楚地了解股息应该如何支付。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Statistics & Risk Modeling
Statistics & Risk Modeling STATISTICS & PROBABILITY-
CiteScore
1.80
自引率
6.70%
发文量
6
期刊介绍: Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.
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