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Modeling The Mortality Trend Under Modern Solvency Regimes

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  • Börger, Matthias
  • Fleischer, Daniel
  • Kuksin, Nikita

Abstract

Stochastic modeling of mortality/longevity risks is necessary for internal models of (re)insurers under the new solvency regimes, such as Solvency II and the Swiss Solvency Test. In this paper, we propose a mortality model which fulfills all requirements imposed by these regimes. We show how the model can be calibrated and applied to the simultaneous modeling of both mortality and longevity risk for several populations. The main contribution of this paper is a stochastic trend component which explicitly models changes in the long-term mortality trend assumption over time. This allows to quantify mortality and longevity risk over the one-year time horizon prescribed by the solvency regimes without relying on nested simulations. We illustrate the practical ability of our model by calculating solvency capital requirements for some example portfolios, and we compare these capital requirements with those from the Solvency II standard formula.

Suggested Citation

  • Börger, Matthias & Fleischer, Daniel & Kuksin, Nikita, 2014. "Modeling The Mortality Trend Under Modern Solvency Regimes," ASTIN Bulletin, Cambridge University Press, vol. 44(1), pages 1-38, January.
    • Handle: RePEc:cup:astinb:v:44:y:2014:i:01:p:1-38_00
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    Citations

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    Cited by:

    1. Börger, Matthias & Russ, Jochen & Schupp, Johannes, 2021. "It takes two: Why mortality trend modeling is more than modeling one mortality trend," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 222-232.
    2. Rokas Gylys & Jonas Šiaulys, 2019. "Revisiting Calibration of the Solvency II Standard Formula for Mortality Risk: Does the Standard Stress Scenario Provide an Adequate Approximation of Value-at-Risk?," Risks, MDPI, vol. 7(2), pages 1-24, May.
    3. Jens Robben & Katrien Antonio & Sander Devriendt, 2022. "Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model," Risks, MDPI, vol. 10(2), pages 1-33, January.
    4. Börger, Matthias & Schupp, Johannes, 2018. "Modeling trend processes in parametric mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 369-380.
    5. Guibert, Quentin & Lopez, Olivier & Piette, Pierrick, 2019. "Forecasting mortality rate improvements with a high-dimensional VAR," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 255-272.
    6. Boonen, Tim J. & De Waegenaere, Anja & Norde, Henk, 2017. "Redistribution of longevity risk: The effect of heterogeneous mortality beliefs," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 175-188.
    7. Li, Hong & De Waegenaere, Anja & Melenberg, Bertrand, 2015. "The choice of sample size for mortality forecasting: A Bayesian learning approach," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 153-168.
    8. Lledó, Josep & Pavía, Jose M. & Morillas-Jurado, Francisco G., 2019. "Incorporating big microdata in life table construction: A hypothesis-free estimator," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 138-150.
    9. Börger, Matthias & Freimann, Arne & Ruß, Jochen, 2021. "A combined analysis of hedge effectiveness and capital efficiency in longevity hedging," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 309-326.
    10. Jose M. Pavía & Josep Lledó, 2022. "Estimation of the combined effects of ageing and seasonality on mortality risk: An application to Spain," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(2), pages 471-497, April.

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