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The Quality of Reserve Risk Calculation Models under Solvency II and IFRS 17

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  • N. Miklós Arató

    (Department of Probability Theory and Statistics, Eötvös Loránd University, Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary
    Department of Mathematics and Computational Sciences, Széchenyi István University, Egyetem tér 1, 9026 Győr, Hungary
    These authors contributed equally to this work.)

  • László Martinek

    (Department of Probability Theory and Statistics, Eötvös Loránd University, Pázmány Péter Sétány 1/C, 1117 Budapest, Hungary
    These authors contributed equally to this work.)

Abstract

We analyse four stochastic claims reserving methods in terms of their capability to estimate reserve risk and how successful they are at predicting distributions and VaRs of claim developments in particular. Both actual data and hypothetical claim triangles support our results. The appropriateness of the Solvency II risk margin on a one-year horizon and of the IFRS 17 risk adjustment in the long run largely vary by the chosen risk model. Despite the fact that IFRS 17 does not uniquely prescribe the metric for risk adjustment, we expect that VaR will be widely applied by insurance firms. Overall, actual data suggest that VaRs are predominantly underestimated by the models. Nevertheless, the 99.5 % -VaRs under Solvency II are mostly sufficient on a 10-year-horizon to cover liabilities.

Suggested Citation

  • N. Miklós Arató & László Martinek, 2022. "The Quality of Reserve Risk Calculation Models under Solvency II and IFRS 17," Risks, MDPI, vol. 10(11), pages 1-13, October.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:11:p:204-:d:953521
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    References listed on IDEAS

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    1. England, P. D. & Verrall, R. J., 2006. "Predictive Distributions of Outstanding Liabilities in General Insurance," Annals of Actuarial Science, Cambridge University Press, vol. 1(2), pages 221-270, September.
    2. Denuit, Michel & Trufin, Julien, 2017. "Beyond the Tweedie Reserving Model: The Collective Approach to Loss Development," LIDAM Reprints ISBA 2017038, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Michel Denuit & Julien Trufin, 2017. "Beyond the Tweedie Reserving Model: The Collective Approach to Loss Development," North American Actuarial Journal, Taylor & Francis Journals, vol. 21(4), pages 611-619, October.
    4. Tilmann Gneiting & Fadoua Balabdaoui & Adrian E. Raftery, 2007. "Probabilistic forecasts, calibration and sharpness," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(2), pages 243-268, April.
    5. Taylor, G. C. & Ashe, F. R., 1983. "Second moments of estimates of outstanding claims," Journal of Econometrics, Elsevier, vol. 23(1), pages 37-61, September.
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