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Extensions on the Hatzopoulos–Sagianou Multiple-Components Stochastic Mortality Model

Author

Listed:
  • Aliki Sagianou

    (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 83200 Samos, Greece)

  • Peter Hatzopoulos

    (Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 83200 Samos, Greece)

Abstract

In this paper, we present extensions of the Hatzopoulos–Sagianou (2020) (HS) multiple-component stochastic mortality model. Our aim is to thoroughly evaluate and stress test the HS model by deploying various link functions, using generalised linear models, and diverse distributions in the model’s estimation method. In this work, we differentiate the HS approach by modelling the number of deaths using the Binomial model commonly employed in the literature of mortality modelling. Given this, new HS extensions are derived using the off-the-shelf link functions, namely the complementary log–log, logit and probit, while we also reform the model by introducing a new form of link functions with a particular focus on the use of heavy-tailed distributions. The above-mentioned enhancements conclude to a new methodology for the HS model, and we prove that it is more suitable than those used in the literature to model the mortality dynamics. In this regard, our work offers an extensive experimental testbed to scrutinise the efficiency, explainability and capacity of the HS model extensions using both the off-the-shelf and the newly introduced form of link functions over datasets with different characteristics. The introduced HS extensions bring an improvement by approximately 16% to the model’s goodness-of-fit, while they uncover more fine-grained age clusters. In addition, we compare the performance of the HS extensions against other well-known mortality models, both under fitting and forecast modes. The results reflect the advantageous features of the HS extensions to deliver a highly informative structure and enable the attribution of an identified mortality trend to a unique age cluster. The above-mentioned improvements enable mortality analysts to perform an in-depth and more detailed investigation of mortality trends for specific age clusters and can contribute to the attempts of academia and industry to tackle the uncertainties and risks introduced by the increasing life expectancy.

Suggested Citation

  • Aliki Sagianou & Peter Hatzopoulos, 2022. "Extensions on the Hatzopoulos–Sagianou Multiple-Components Stochastic Mortality Model," Risks, MDPI, vol. 10(7), pages 1-30, June.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:7:p:131-:d:843949
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    References listed on IDEAS

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    1. Hunt, Andrew & Villegas, Andrés M., 2015. "Robustness and convergence in the Lee–Carter model with cohort effects," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 186-202.
    2. Booth, H. & Tickle, L., 2008. "Mortality Modelling and Forecasting: a Review of Methods," Annals of Actuarial Science, Cambridge University Press, vol. 3(1-2), pages 3-43, September.
    3. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    4. Philippe Deprez & Pavel V. Shevchenko & Mario V. Wuthrich, 2017. "Machine Learning Techniques for Mortality Modeling," Papers 1705.03396, arXiv.org.
    5. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
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