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Estimating extreme cancellation rates in life insurance

Author

Listed:
  • Francesca Biagini
  • Tobias Huber
  • Johannes G. Jaspersen
  • Andrea Mazzon

Abstract

This paper assesses the risk of a mass lapse event in life insurance. The rarity of the event and the complexity of policyholder behavior make the risk assessment of such a scenario difficult. Using a simulation study, we evaluate how different estimation methods can assess the risk of this scenario, using panel data at the company level. We then use the best‐performing method to estimate the probability distribution function of a mass cancellation event in the United States and Germany. We identify dependencies of the event on company and country characteristics, which have not been taken into account by regulating agencies. We also find that the current mass lapse scenario in Solvency II has no empirical foundation for the German market. We show that an empirically valid scenario leads to a significantly lower solvency capital requirement for the average German life insurer.

Suggested Citation

  • Francesca Biagini & Tobias Huber & Johannes G. Jaspersen & Andrea Mazzon, 2021. "Estimating extreme cancellation rates in life insurance," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 88(4), pages 971-1000, December.
    • Handle: RePEc:bla:jrinsu:v:88:y:2021:i:4:p:971-1000
    DOI: 10.1111/jori.12336
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    References listed on IDEAS

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    Cited by:

    1. Kubitza, Christian & Grochola, Nicolaus & Gründl, Helmut, 2021. "Life insurance convexity," ICIR Working Paper Series 42/21, Goethe University Frankfurt, International Center for Insurance Regulation (ICIR).
    2. Cheng, Chunli & Hilpert, Christian & Miri Lavasani, Aidin & Schaefer, Mick, 2023. "Surrender contagion in life insurance," European Journal of Operational Research, Elsevier, vol. 305(3), pages 1465-1479.
    3. Siti Nurasyikin Shamsuddin & Noriszura Ismail & Nur Firyal Roslan, 2022. "What We Know about Research on Life Insurance Lapse: A Bibliometric Analysis," Risks, MDPI, vol. 10(5), pages 1-19, May.
    4. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation In Extreme Value Regression Models Of Hedge Fund Tail Risks," Working Papers hal-04090916, HAL.
    5. Julien Hambuckers & Marie Kratz & Antoine Usseglio-Carleve, 2023. "Efficient Estimation in Extreme Value Regression Models of Hedge Fund Tail Risks," Papers 2304.06950, arXiv.org.

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