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Sarmanov distribution for modeling dependence between the frequency and the average severity of insurance claims

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  • Vernic, Raluca
  • Bolancé, Catalina
  • Alemany, Ramon

Abstract

Real data studies emphasized situations where the classical independence assumption between the frequency and the severity of claims does not hold in the collective model. Therefore, there is an increasing interest in defining models that capture this dependence. In this paper, we introduce such a model based on Sarmanov's bivariate distribution, which has the ability of joining different types of marginals in flexible dependence structures. More precisely, we join the claims frequency and the average severity by means of this distribution. We also suggest a maximum likelihood estimation procedure to estimate the parameters and illustrate it both on simulated and real data.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:insuma:v:102:y:2022:i:c:p:111-125
    DOI: 10.1016/j.insmatheco.2021.12.001
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    References listed on IDEAS

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    Cited by:

    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. 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).

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