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Research Article |

Investigating the Impact of Variable Dividends and Tail Dependence in a Compound Poisson Risk Model

This paper extends the compound Poisson risk model with a variable threshold dividend payment strategy and dependence between claims and inter-claim times, modeled via the Spearman copula. The objective is to establish the ultimate ruin probability in this framework. Following an introduction that motivates the study and highlights limitations of traditional risk models, the paper reviews relevant literature on risk models, dividend strategies, and copulas. Subsequently, it describes the extended model, including the dividend strategy and dependence structure. The Gerber-Shiu transform and Laplace transform of the ruin probability are then derived. Finally, the ultimate ruin probability is determined within the proposed model. Concluding remarks discuss the implications of the findings and suggest directions for future research. By considering a more realistic and comprehensive approach to financial risk modeling in insurance, this paper aims to contribute to the field of insurance risk management and provide industry professionals with improved tools for risk assessment and management.

Gerber-Shiu Function, Copula, Integro-Differential Equation, Ruin Probability

APA Style

Ouedraogo, K. M., Kafando, D. A., Ouedraogo, F. X., Nitiema, P. C. (2024). Investigating the Impact of Variable Dividends and Tail Dependence in a Compound Poisson Risk Model. International Journal of Statistical Distributions and Applications, 10(1), 1-9. https://doi.org/10.11648/j.ijsd.20241001.11

ACS Style

Ouedraogo, K. M.; Kafando, D. A.; Ouedraogo, F. X.; Nitiema, P. C. Investigating the Impact of Variable Dividends and Tail Dependence in a Compound Poisson Risk Model. Int. J. Stat. Distrib. Appl. 2024, 10(1), 1-9. doi: 10.11648/j.ijsd.20241001.11

AMA Style

Ouedraogo KM, Kafando DA, Ouedraogo FX, Nitiema PC. Investigating the Impact of Variable Dividends and Tail Dependence in a Compound Poisson Risk Model. Int J Stat Distrib Appl. 2024;10(1):1-9. doi: 10.11648/j.ijsd.20241001.11

Copyright © 2024 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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