IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v467y2024ics0096300323006616.html
   My bibliography  Save this article

Compound Poisson–Lindley process with Sarmanov dependence structure and its application for premium-based spectral risk forecasting

Author

Listed:
  • Syuhada, Khreshna
  • Tjahjono, Venansius
  • Hakim, Arief

Abstract

One of the fundamental challenges insurance companies face is forecasting a fair premium that covers the cost of claims while maintaining profitability. To comprehend the risk of insurance claims, one needs to construct a collective risk model. In this study, we aim to propose a new collective risk model, namely a dependent compound Poisson–Lindley process. We capture the dependence structure between the claim frequency and severity using a bivariate Sarmanov distribution. We then employ this model to perform premium-based risk measure forecasting such that the insurance company can obtain the premium value for the policyholder. We accomplish this task by proposing a specific risk spectrum to adjust the premium for risk-aversion-type insurance companies. Our main results demonstrate that a collective risk model based on the Poisson–Lindley process is able to capture the overdispersion phenomenon in claim frequency that is common in practice. This ability is confirmed by our simulation conducted using real insurance data. Furthermore, when accounting for the Sarmanov dependence structure, the resulting premium value becomes more appropriate.

Suggested Citation

  • Syuhada, Khreshna & Tjahjono, Venansius & Hakim, Arief, 2024. "Compound Poisson–Lindley process with Sarmanov dependence structure and its application for premium-based spectral risk forecasting," Applied Mathematics and Computation, Elsevier, vol. 467(C).
    • Handle: RePEc:eee:apmaco:v:467:y:2024:i:c:s0096300323006616
    DOI: 10.1016/j.amc.2023.128492
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323006616
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2023.128492?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Matyska, Branka, 2021. "Salience, systemic risk and spectral risk measures as capital requirements," Journal of Economic Dynamics and Control, Elsevier, vol. 125(C).
    2. Vernic, Raluca, 2018. "On the evaluation of some multivariate compound distributions with Sarmanov’s counting distribution," Insurance: Mathematics and Economics, Elsevier, vol. 79(C), pages 184-193.
    3. Dheeraj Goyal & Nil Kamal Hazra & Maxim Finkelstein, 2022. "On Properties of the Phase-type Mixed Poisson Process and its Applications to Reliability Shock Modeling," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2933-2960, December.
    4. Garrido, J. & Genest, C. & Schulz, J., 2016. "Generalized linear models for dependent frequency and severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 205-215.
    5. Acerbi, Carlo, 2002. "Spectral measures of risk: A coherent representation of subjective risk aversion," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1505-1518, July.
    6. Tzougas, George, 2020. "EM estimation for the Poisson-Inverse Gamma regression model with varying dispersion: an application to insurance ratemaking," LSE Research Online Documents on Economics 106539, London School of Economics and Political Science, LSE Library.
    7. Park, Kyunghyun & Wong, Hoi Ying & Yan, Tingjin, 2023. "Robust retirement and life insurance with inflation risk and model ambiguity," Insurance: Mathematics and Economics, Elsevier, vol. 110(C), pages 1-30.
    8. Lee, Gee Y. & Shi, Peng, 2019. "A dependent frequency–severity approach to modeling longitudinal insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 87(C), pages 115-129.
    9. M. S. Aminzadeh & Min Deng, 2021. "Bayesian estimation of ruin probability based on NHPP claim arrivals and Inverse-Gaussian distributed claim aggregates," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(17), pages 4096-4118, August.
    10. Claudia Czado & Rainer Kastenmeier & Eike Brechmann & Aleksey Min, 2012. "A mixed copula model for insurance claims and claim sizes," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2012(4), pages 278-305.
    11. Yinzhi Wang & Ingrid Hobæk Haff, 2019. "Focussed selection of the claim severity distribution," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2019(2), pages 129-142, February.
    12. Cha, Ji Hwan, 2019. "Poisson Lindley process and its main properties," Statistics & Probability Letters, Elsevier, vol. 152(C), pages 74-81.
    13. Cotter, John & Dowd, Kevin, 2006. "Extreme spectral risk measures: An application to futures clearinghouse margin requirements," Journal of Banking & Finance, Elsevier, vol. 30(12), pages 3469-3485, December.
    14. Tzougas, George & Pignatelli di Cerchiara, Alice, 2021. "The multivariate mixed Negative Binomial regression model with an application to insurance a posteriori ratemaking," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 602-625.
    15. Vernic, Raluca & Bolancé, Catalina & Alemany, Ramon, 2022. "Sarmanov distribution for modeling dependence between the frequency and the average severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 111-125.
    16. Gendron, Michel & Crepeau, Helene, 1989. "On the computation of the aggregate claim distribution when individual claims are Inverse Gaussian," Insurance: Mathematics and Economics, Elsevier, vol. 8(3), pages 251-258, November.
    17. Catalina Bolancé & Raluca Vernic, 2020. "Frequency and Severity Dependence in the Collective Risk Model: An Approach Based on Sarmanov Distribution," Mathematics, MDPI, vol. 8(9), pages 1-17, August.
    18. Chaubey, Yogendra P. & Garrido, Jose & Trudeau, Sonia, 1998. "On the computation of aggregate claims distributions: some new approximations," Insurance: Mathematics and Economics, Elsevier, vol. 23(3), pages 215-230, December.
    19. Eling, Martin, 2012. "Fitting insurance claims to skewed distributions: Are the skew-normal and skew-student good models?," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 239-248.
    20. Guo, Fenglong, 2022. "Ruin probability of a continuous-time model with dependence between insurance and financial risks caused by systematic factors," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    21. Zhang, Xiaoyu & Xu, Maochao & Su, Jianxi & Zhao, Peng, 2023. "Structural models for fog computing based internet of things architectures with insurance and risk management applications," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1273-1291.
    22. Cheung, Eric C.K. & Rabehasaina, Landy & Woo, Jae-Kyung & Xu, Ran, 2019. "Asymptotic correlation structure of discounted Incurred But Not Reported claims under fractional Poisson arrival process," European Journal of Operational Research, Elsevier, vol. 276(2), pages 582-601.
    23. Marri, Fouad & Furman, Edward, 2012. "Pricing compound Poisson processes with the Farlie–Gumbel–Morgenstern dependence structure," Insurance: Mathematics and Economics, Elsevier, vol. 51(1), pages 151-157.
    24. George Tzougas, 2020. "EM Estimation for the Poisson-Inverse Gamma Regression Model with Varying Dispersion: An Application to Insurance Ratemaking," Risks, MDPI, vol. 8(3), pages 1-23, September.
    25. Hou, Yanxi, 2022. "A two-stage model for high-risk prediction in insurance ratemaking: Asymptotics and inference," Insurance: Mathematics and Economics, Elsevier, vol. 104(C), pages 283-301.
    26. Gao, Guangyuan & Li, Jiahong, 2023. "Dependence modeling of frequency-severity of insurance claims using waiting time," Insurance: Mathematics and Economics, Elsevier, vol. 109(C), pages 29-51.
    27. Avanzi, Benjamin & Wong, Bernard & Yang, Xinda, 2016. "A micro-level claim count model with overdispersion and reporting delays," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 1-14.
    28. Krämer, Nicole & Brechmann, Eike C. & Silvestrini, Daniel & Czado, Claudia, 2013. "Total loss estimation using copula-based regression models," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 829-839.
    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. Övgücan Karadağ Erdemir, 2023. "A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models," EKOIST Journal of Econometrics and Statistics, Istanbul University, Faculty of Economics, vol. 0(39), pages 161-171, December.
    2. Michel Denuit & Yang Lu, 2021. "Wishart‐gamma random effects models with applications to nonlife insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(2), pages 443-481, June.
    3. Oh, Rosy & Jeong, Himchan & Ahn, Jae Youn & Valdez, Emiliano A., 2021. "A multi-year microlevel collective risk model," Insurance: Mathematics and Economics, Elsevier, vol. 100(C), pages 309-328.
    4. Verschuren, Robert Matthijs, 2022. "Frequency-severity experience rating based on latent Markovian risk profiles," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 379-392.
    5. Denuit, Michel & Lu, Yang, 2020. "Wishart-Gamma mixtures for multiperil experience ratemaking, frequency-severity experience rating and micro-loss reserving," LIDAM Discussion Papers ISBA 2020016, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    6. Vernic, Raluca & Bolancé, Catalina & Alemany, Ramon, 2022. "Sarmanov distribution for modeling dependence between the frequency and the average severity of insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 102(C), pages 111-125.
    7. Reijnen, Rajko & Albers, Willem & Kallenberg, Wilbert C.M., 2005. "Approximations for stop-loss reinsurance premiums," Insurance: Mathematics and Economics, Elsevier, vol. 36(3), pages 237-250, June.
    8. Wentao Hu & Cuixia Chen & Yufeng Shi & Ze Chen, 2022. "A Tail Measure With Variable Risk Tolerance: Application in Dynamic Portfolio Insurance Strategy," Methodology and Computing in Applied Probability, Springer, vol. 24(2), pages 831-874, June.
    9. Massimiliano Barbi & Silvia Romagnoli, 2016. "Optimal hedge ratio under a subjective re-weighting of the original measure," Applied Economics, Taylor & Francis Journals, vol. 48(14), pages 1271-1280, March.
    10. Ibragimov, Rustam & Walden, Johan, 2007. "The limits of diversification when losses may be large," Scholarly Articles 2624460, Harvard University Department of Economics.
    11. Wächter, Hans Peter & Mazzoni, Thomas, 2013. "Consistent modeling of risk averse behavior with spectral risk measures," European Journal of Operational Research, Elsevier, vol. 229(2), pages 487-495.
    12. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    13. Sarra Ghaddab & Manel Kacem & Christian Peretti & Lotfi Belkacem, 2023. "Extreme severity modeling using a GLM-GPD combination: application to an excess of loss reinsurance treaty," Empirical Economics, Springer, vol. 65(3), pages 1105-1127, September.
    14. Cotter, John & Dowd, Kevin, 2006. "Spectral Risk Measures with an Application to Futures Clearinghouse Variation Margin Requirements," MPRA Paper 3495, University Library of Munich, Germany.
    15. Cotter, John & Dowd, Kevin, 2007. "Evaluating the Precision of Estimators of Quantile-Based Risk Measures," MPRA Paper 3504, University Library of Munich, Germany.
    16. Catalina Bolancé & Raluca Vernic, 2020. "Frequency and Severity Dependence in the Collective Risk Model: An Approach Based on Sarmanov Distribution," Mathematics, MDPI, vol. 8(9), pages 1-17, August.
    17. Tzougas, George & Jeong, Himchan, 2021. "An expectation-maximization algorithm for the exponential-generalized inverse Gaussian regression model with varying dispersion and shape for modelling the aggregate claim amount," LSE Research Online Documents on Economics 108210, London School of Economics and Political Science, LSE Library.
    18. George Tzougas & Himchan Jeong, 2021. "An Expectation-Maximization Algorithm for the Exponential-Generalized Inverse Gaussian Regression Model with Varying Dispersion and Shape for Modelling the Aggregate Claim Amount," Risks, MDPI, vol. 9(1), pages 1-17, January.
    19. Gao, Guangyuan & Li, Jiahong, 2023. "Dependence modeling of frequency-severity of insurance claims using waiting time," Insurance: Mathematics and Economics, Elsevier, vol. 109(C), pages 29-51.
    20. Dong-Young Lim, 2021. "A Neural Frequency-Severity Model and Its Application to Insurance Claims," Papers 2106.10770, arXiv.org, revised Feb 2024.

    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:eee:apmaco:v:467:y:2024:i:c:s0096300323006616. 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.