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An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing

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  • Apostolos Bozikas

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece)

  • Georgios Pitselis

    (Department of Statistics and Insurance Science, University of Piraeus, 18534 Piraeus, Greece)

Abstract

During the last decades, life expectancy has risen significantly in the most developed countries all over the world. Greece is a case in point; consequently, higher governmental financial responsibilities occur as well as serious concerns are raised owing to population ageing. To address this issue, an efficient forecasting method is required. Therefore, the most important stochastic models were comparatively applied to Greek data for the first time. An analysis of their fitting behaviour by gender was conducted and the corresponding forecasting results were evaluated. In particular, we incorporated the Greek population data into seven stochastic mortality models under a common age-period-cohort framework. The fitting performance of each model was thoroughly evaluated based on information criteria values as well as the likelihood ratio test and their robustness to period changes was investigated. In addition, parameter risk in forecasts was assessed by employing bootstrapping techniques. For completeness, projection results for both genders were also illustrated in pricing insurance-related products.

Suggested Citation

  • Apostolos Bozikas & Georgios Pitselis, 2018. "An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing," Risks, MDPI, vol. 6(2), pages 1-34, April.
    • Handle: RePEc:gam:jrisks:v:6:y:2018:i:2:p:44-:d:142754
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    References listed on IDEAS

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    Cited by:

    1. Bozikas, Apostolos & Pitselis, Georgios, 2020. "Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 353-368.
    2. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    3. Ana Debón & Steven Haberman & Francisco Montes & Edoardo Otranto, 2021. "Do Different Models Induce Changes in Mortality Indicators? That Is a Key Question for Extending the Lee-Carter Model," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    4. Apostolos Bozikas & Georgios Pitselis, 2019. "Credible Regression Approaches to Forecast Mortality for Populations with Limited Data," Risks, MDPI, vol. 7(1), pages 1-22, February.

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