IDEAS home Printed from https://ideas.repec.org/a/wly/jjmath/v2022y2022i1n2771223.html

Reducing the Possibility of Ruin by Maximizing the Survival Function for the Insurance Company’s Portfolio

Author

Listed:
  • Masoud Komunte
  • Christian Kasumo
  • Verdiana Grace Masanja

Abstract

In this paper, the intention was to reduce the possibility of ruin in the insurance company by maximizing its survival function. This paper uses a perturbed classical risk process as the basic model. The basic model was later compounded by refinancing and return on investment. The Hamilton–Jacobi–Bellman equation and integro‐differential equation of Volterra type were obtained. The Volterra integro‐differential equation for the survival function of the insurance company was converted to a third‐order ordinary differential equation which was later converted into a system of first‐order ordinary differential equations. This system was then solved numerically using the fourth‐order Runge‐Kutta method. The results show that the survival function increases with the increase in the intensity of the counting process but decreases with an increase in the instantaneous rate of stock return and return volatility. This is due to the fact that the insurance company faces more risk. Thus, this paper suggests that in this situation, more investments should be made in risk‐free assets.

Suggested Citation

  • Masoud Komunte & Christian Kasumo & Verdiana Grace Masanja, 2022. "Reducing the Possibility of Ruin by Maximizing the Survival Function for the Insurance Company’s Portfolio," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
  • Handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:2771223
    DOI: 10.1155/2022/2771223
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2022/2771223
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2022/2771223?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Lesław Gajek & Dariusz Zagrodny, 2004. "Reinsurance Arrangements Maximizing Insurer's Survival Probability," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 71(3), pages 421-435, September.
    2. Jostein Paulsen & Bo Normann Rasmussen, 2003. "Simulating Ruin Probabilities for a Class of Semimartingales by Importance Sampling Methods," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2003(3), pages 178-216.
    3. Paulsen, Jostein & Kasozi, Juma & Steigen, Andreas, 2005. "A numerical method to find the probability of ultimate ruin in the classical risk model with stochastic return on investments," Insurance: Mathematics and Economics, Elsevier, vol. 36(3), pages 399-420, June.
    4. Christian Kasumo & Juma Kasozi & Dmitry Kuznetsov, 2018. "On Minimizing the Ultimate Ruin Probability of an Insurer by Reinsurance," Journal of Applied Mathematics, Hindawi, vol. 2018, pages 1-11, February.
    5. Christian Kasumo & Juma Kasozi & Dmitry Kuznetsov, 2018. "On Minimizing the Ultimate Ruin Probability of an Insurer by Reinsurance," Journal of Applied Mathematics, John Wiley & Sons, vol. 2018(1).
    6. Badaoui, Mohamed & Fernández, Begoña, 2013. "An optimal investment strategy with maximal risk aversion and its ruin probability in the presence of stochastic volatility on investments," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 1-13.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guerra, M. & de Moura, A.B., 2021. "Reinsurance of multiple risks with generic dependence structures," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 547-571.
    2. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.
    3. Dimitrova, Dimitrina S. & Kaishev, Vladimir K., 2010. "Optimal joint survival reinsurance: An efficient frontier approach," Insurance: Mathematics and Economics, Elsevier, vol. 47(1), pages 27-35, August.
    4. Bahman Angoshtari & Virginia R. Young, 2020. "Optimal Insurance to Minimize the Probability of Ruin: Inverse Survival Function Formulation," Papers 2012.03798, arXiv.org.
    5. Cheung, Ka Chun & Phillip Yam, Sheung Chi & Yuen, Fei Lung & Zhang, Yiying, 2020. "Concave distortion risk minimizing reinsurance design under adverse selection," Insurance: Mathematics and Economics, Elsevier, vol. 91(C), pages 155-165.
    6. Yin, Chuancun & Wen, Yuzhen, 2013. "An extension of Paulsen–Gjessing’s risk model with stochastic return on investments," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 469-476.
    7. Liang, Xiaoqing & Young, Virginia R., 2018. "Minimizing the probability of ruin: Optimal per-loss reinsurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 181-190.
    8. Zhuo Jin & Zuo Quan Xu & Bin Zou, 2023. "Optimal moral-hazard-free reinsurance under extended distortion premium principles," Papers 2304.08819, arXiv.org.
    9. Cui, Wei & Yang, Jingping & Wu, Lan, 2013. "Optimal reinsurance minimizing the distortion risk measure under general reinsurance premium principles," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 74-85.
    10. Xu, Lin & Zhang, Liming & Yao, Dingjun, 2017. "Optimal investment and reinsurance for an insurer under Markov-modulated financial market," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 7-19.
    11. Tan, Ken Seng & Wei, Pengyu & Wei, Wei & Zhuang, Sheng Chao, 2020. "Optimal dynamic reinsurance policies under a generalized Denneberg’s absolute deviation principle," European Journal of Operational Research, Elsevier, vol. 282(1), pages 345-362.
    12. Carole Bernard & Weidong Tian, 2010. "Insurance Market Effects of Risk Management Metrics," The Geneva Risk and Insurance Review, Palgrave Macmillan;International Association for the Study of Insurance Economics (The Geneva Association), vol. 35(1), pages 47-80, June.
    13. Guerra, Manuel & Centeno, M.L., 2012. "Are quantile risk measures suitable for risk-transfer decisions?," Insurance: Mathematics and Economics, Elsevier, vol. 50(3), pages 446-461.
    14. Jostein Paulsen, 2008. "Ruin models with investment income," Papers 0806.4125, arXiv.org, revised Dec 2008.
    15. Flores, Eduardo & de Carvalho, João Vinicius França & Sampaio, Joelson Oliveira, 2021. "Impact of interest rates on the life insurance market development: Cross-country evidence," Research in International Business and Finance, Elsevier, vol. 58(C).
    16. Nian Yao & Zhiming Yang, 2017. "Optimal excess-of-loss reinsurance and investment problem for an insurer with default risk under a stochastic volatility model," Papers 1704.08234, arXiv.org.
    17. Jianfa Cong & Ken Tan, 2016. "Optimal VaR-based risk management with reinsurance," Annals of Operations Research, Springer, vol. 237(1), pages 177-202, February.
    18. Mathieu Gatumel & Sabine Lemoyne de Forges, 2013. "Understanding and Monitoring Reinsurance Counterparty Risk," Post-Print hal-00946934, HAL.
    19. Guan, Guohui & Liang, Zongxia, 2014. "Optimal reinsurance and investment strategies for insurer under interest rate and inflation risks," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 105-115.
    20. Centeno, M.L. & Guerra, M., 2010. "The optimal reinsurance strategy -- the individual claim case," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 450-460, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:jjmath:v:2022:y:2022:i:1:n:2771223. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/1469 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.