Dividend maximization in a hidden Markov switching model

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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Optimising dividends and consumption under an exponential CIR as a discount factor

Mathematical Methods of Operations Research ◽

10.1007/s00186-020-00714-w ◽

2020 ◽

Vol 92 (2) ◽

pp. 285-309

Author(s):

Julia Eisenberg ◽

Yuliya Mishura

Keyword(s):

Brownian Motion ◽

Optimal Strategy ◽

Value Function ◽

Insurance Company ◽

Parameter Choice ◽

Surplus Process ◽

Dividend Payments ◽

Cir Process ◽

The Value Function ◽

Special Parameter

AbstractWe consider an economic agent (a household or an insurance company) modelling its surplus process by a deterministic process or by a Brownian motion with drift. The goal is to maximise the expected discounted spending/dividend payments under a discounting factor given by an exponential CIR process. In the deterministic case, we are able to find explicit expressions for the optimal strategy and the value function. For the Brownian motion case, we are able to show that for a special parameter choice the optimal strategy is a constant-barrier strategy.

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Optimal dividends and capital injection under dividend restrictions

Mathematical Methods of Operations Research ◽

10.1007/s00186-020-00720-y ◽

2020 ◽

Vol 92 (3) ◽

pp. 461-487 ◽

Cited By ~ 2

Author(s):

Kristoffer Lindensjö ◽

Filip Lindskog

Keyword(s):

Optimal Strategy ◽

Stopping Time ◽

Type I ◽

Insurance Company ◽

Type Ii ◽

Dividend Payout ◽

Optimal Dividends ◽

Capital Injection ◽

Surplus Process ◽

Dividend Payments

AbstractWe study a singular stochastic control problem faced by the owner of an insurance company that dynamically pays dividends and raises capital in the presence of the restriction that the surplus process must be above a given dividend payout barrier in order for dividend payments to be allowed. Bankruptcy occurs if the surplus process becomes negative and there are proportional costs for capital injection. We show that one of the following strategies is optimal: (i) Pay dividends and inject capital in order to reflect the surplus process at an upper barrier and at 0, implying bankruptcy never occurs. (ii) Pay dividends in order to reflect the surplus process at an upper barrier and never inject capital—corresponding to absorption at 0—implying bankruptcy occurs the first time the surplus reaches zero. We show that if the costs of capital injection are low, then a sufficiently high dividend payout barrier will change the optimal strategy from type (i) (without bankruptcy) to type (ii) (with bankruptcy). Moreover, if the costs are high, then the optimal strategy is of type (ii) regardless of the dividend payout barrier. We also consider the possibility for the owner to choose a stopping time at which the insurance company is liquidated and the owner obtains a liquidation value. The uncontrolled surplus process is a Wiener process with drift.

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DIVIDEND OPTIMIZATION FOR A REGIME-SWITCHING DIFFUSION MODEL WITH RESTRICTED DIVIDEND RATES

Astin Bulletin ◽

10.1017/asb.2014.2 ◽

2014 ◽

Vol 44 (2) ◽

pp. 459-494 ◽

Cited By ~ 6

Author(s):

Jinxia Zhu

Keyword(s):

Markov Chain ◽

Regime Switching ◽

Critical Level ◽

Sufficient Conditions ◽

Insurance Company ◽

Threshold Strategy ◽

Regime Switching Model ◽

Switching Model ◽

Switching Diffusion ◽

Optimal Dividend

AbstractWe consider the optimal dividend control problem to find an optimal strategy under the constraint that dividend rates is restricted such that the expected total discounted dividends are maximized for an insurance company. The evolution of the reserve is modeled by a diffusion process with drift and volatility coefficients modulated by an observable Markov chain. We consider the regime-switching threshold strategy which pays out dividends at the maximal possible rate when the current reserve is above some critical level dependent on the regime of the Markov chain at the time, and pays nothing when the reserve is below that level. We give sufficient conditions under which such type of strategy is optimal for the regime-switching model.

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Statistical Inference for Coherent Systems from Partial Information and Markov Chain Monte Carlo Methods.

10.21236/ada305676 ◽

1996 ◽

Author(s):

Hani Doss

Keyword(s):

Monte Carlo ◽

Markov Chain ◽

Markov Chain Monte Carlo ◽

Statistical Inference ◽

Monte Carlo Methods ◽

Partial Information ◽

Coherent Systems

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Regimes in CDS Spreads: A Markov Switching Model of iTraxx Europe Indices

SSRN Electronic Journal ◽

10.2139/ssrn.928352 ◽

2006 ◽

Cited By ~ 3

Author(s):

Carol Alexander ◽

Andreas Kaeck

Keyword(s):

Markov Switching ◽

Markov Switching Model ◽

Switching Model

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Stock Market Volatility and Terrorism: New Evidence from the Markov Switching Model

Peace Economics Peace Science and Public Policy ◽

10.1515/peps-2020-0005 ◽

2020 ◽

Vol 0 (0) ◽

Author(s):

Faheem Aslam ◽

Hyoung-Goo Kang ◽

Khurrum Shahzad Mughal ◽

Tahir Mumtaz Awan ◽

Yasir Tariq Mohmand

Keyword(s):

Stock Market ◽

Significant Risk ◽

Market Volatility ◽

Terrorist Attacks ◽

Stock Market Volatility ◽

Markov Switching Model ◽

Physical Damage ◽

Stock Market Returns ◽

Switching Model ◽

The Impact

AbstractTerrorism in Pakistan poses a significant risk towards the lives of people by violent destruction and physical damage. In addition to human loss, such catastrophic activities also affect the financial markets. The purpose of this study is to examine the impact of terrorism on the volatility of the Pakistan stock market. The financial impact of 339 terrorist attacks for a period of 18 years (2000–2018) is estimated w.r.t. target type, days of the week, and surprise factor. Three important macroeconomic variables namely exchange rate, gold, and oil were also considered. The findings of the EGARCH (1, 1) model revealed that the terrorist attacks targeting the security forces and commercial facilities significantly increased the stock market volatility. The significant impact of terrorist attacks on Monday, Tuesday, and Thursday confirms the overreaction of investors to terrorist news. Furthermore, the results confirmed the negative linkage between the surprise factor and stock market returns. The findings of this study have significant implications for investors and policymakers.

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Optimal Dividend and Capital Injection Problem with Transaction Cost and Salvage Value: The Case of Excess-of-Loss Reinsurance Based on the Symmetry of Risk Information

Symmetry ◽

10.3390/sym10070276 ◽

2018 ◽

Vol 10 (7) ◽

pp. 276 ◽

Cited By ~ 1

Author(s):

Qingyou Yan ◽

Le Yang ◽

Tomas Baležentis ◽

Dalia Streimikiene ◽

Chao Qin

Keyword(s):

Transaction Cost ◽

Risk Information ◽

Insurance Company ◽

Optimal Control Strategy ◽

Ruin Time ◽

Discounted Cost ◽

Capital Injection ◽

Surplus Process ◽

Optimal Dividend ◽

A Company

This paper considers the optimal dividend and capital injection problem for an insurance company, which controls the risk exposure by both the excess-of-loss reinsurance and capital injection based on the symmetry of risk information. Besides the proportional transaction cost, we also incorporate the fixed transaction cost incurred by capital injection and the salvage value of a company at the ruin time in order to make the surplus process more realistic. The main goal is to maximize the expected sum of the discounted salvage value and the discounted cumulative dividends except for the discounted cost of capital injection until the ruin time. By considering whether there is capital injection in the surplus process, we construct two instances of suboptimal models and then solve for the corresponding solution in each model. Lastly, we consider the optimal control strategy for the general model without any restriction on the capital injection or the surplus process.

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HYDROLOGICAL DROUGHT FORECASTS USING MARKOV CHAINS AND REGRESSION MODELS (A CASE STUDY FOR NORTHWEST OF ALGERIA)

Meteorologiya i Gidrologiya ◽

10.52002/0130-2906-2021-10-90-103 ◽

2021 ◽

pp. 90-103

Author(s):

A. RAHMOUNI ◽

◽

M. MEDDI ◽

A. HAMOUDI SAAED ◽

◽

...

Keyword(s):

Lead Time ◽

Robust Regression ◽

Forecast Accuracy ◽

Hydrological Drought ◽

Lead Times ◽

Chain Process ◽

Markov Switching Model ◽

North West ◽

Switching Model ◽

The North

An effective drought forecast is an important measure to mitigate some of its most damaging impacts. In this study we compare the effectiveness of two models: Markov Switching Model (MSM) and Robust Regression Model (RRM) with three different approaches to forecast hydrological drought events in the north-west of Algeria using Standardized Runoff Index (SRI). The validation of these models is carried out by hydro-climatic series of 41 stations for the period of 1968-2009. The values of SRI 3, SRI 6, and SRI 12 have been forecasted over lead times of 1 and 6 months. The performance of forecast results is measured using R2 and RMSE. For the lead time of 1 month, the results are quite similar for both models with slight superiority for the Markov chain process. The addition of the SPI or RDI indices as independent variables improves this performance for some stations while it decreases accuracy for other stations. However, forecast accuracy declines significantly as the lead time increases to 6 months particularly for regression results.

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Online to offline social interaction on gaming motivations

Kybernetes ◽

10.1108/k-02-2021-0156 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Wei-Lun Chang ◽

Li-Ming Chen ◽

Yen-Hao Hsieh

Keyword(s):

Markov Chain ◽

Online Games ◽

Online Gaming ◽

Online Game ◽

Content Type ◽

Switching Model ◽

First Order ◽

Order Markov Chain ◽

Game Players ◽

Motivation Model

PurposeThis research examined the social interactions of online game players based on the proposed motivation model in order to understand the transitions of motivation of online game. The authors also separated samples into four categories to compare the difference of different type of online game players.Design/methodology/approachThis study proposed a motivation model for online game player based on existence–relatedness–growth theory. The authors also analyze the transitions of motivations via first-order and second-order Markov chain switching model to obtain the journey of online to offline socialization.FindingsTeamwork–socialization players preferred to make friends in their online gaming network to socialize. Competition–socialization players were mostly students who played games to compete and socialize and may share experience in online or offline activities. Teamwork–mechanics players purely derived pleasure from gaming and were not motivated by other factors in their gaming activities. Competition–mechanics players may already have friends with other gamers in real life.Research limitations/implicationsMore samples can be added to generate more generalizable findings and the proposed motivation model can be extended by other motivations related to online gaming behavior. The authors proposed a motivation model for online to offline socialization and separated online game players into four categories: teamwork–socialization, competition–socialization, teamwork–mechanics and competition–mechanics. The category of teamwork–socialization may contribute to online to offline socialization area. The category of competition–mechanics may add value to the area of traditional offline socialization. The categories of competition–socialization and teamwork–mechanics may help extant literature understand critical stimulus for online gaming behavior.Practical implicationsThe authors’ findings can help online gaming industry understand the motivation journey of players through transition. Different types of online games may have various online game player's journey that can assist companies in improving the quality of online games. Online game companies can also offer official community to players for further interaction and experience exchange or the platform for offline activities in the physical environment.Originality/valueThis research proposed a novel motivation model to examine online to offline socializing behavior for online game research. The motivations in model were interconnected via the support of literature. The authors also integrated motivations by Markov chain switching model to obtain the transitions of motivational status. It is also the first attempt to analyze first-order and second-order Markov chain switching model for analysis. The authors’ research examined the interconnected relationships among motivations in addition to the influential factors to online gaming behavior from previous research. The results may contribute to extend the understanding of online to offline socialization in online gaming literature.

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