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A Comparison of Forecasting Mortality Models Using Resampling Methods

Author

Listed:
  • David Atance

    (Departamento de Economía y Dirección de Empresas, Universidad de Alcalá, Pza. San Diego S/N, 28801 Alcalá de Henares, Spain
    These authors contributed equally to this work.)

  • Ana Debón

    (Centro de Gestión de la Calidad y del Cambio, Universitat Politècnica de València, Camino de Vera, S/N, 46022 Valencia, Spain
    These authors contributed equally to this work.)

  • Eliseo Navarro

    (Departamento de Economía y Dirección de Empresas, Universidad de Alcalá, Pza. San Diego S/N, 28801 Alcalá de Henares, Spain
    These authors contributed equally to this work.)

Abstract

The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies’ profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee–Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.

Suggested Citation

  • David Atance & Ana Debón & Eliseo Navarro, 2020. "A Comparison of Forecasting Mortality Models Using Resampling Methods," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:9:p:1550-:d:411425
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    References listed on IDEAS

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