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A Neural-Network Analyzer For Mortality Forecast

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  • Hainaut, Donatien

Abstract

This article proposes a neural-network approach to predict and simulate human mortality rates. This semi-parametric model is capable to detect and duplicate non-linearities observed in the evolution of log-forces of mortality. The method proceeds in two steps. During the first stage, a neural-network-based generalization of the principal component analysis summarizes the information carried by the surface of log-mortality rates in a small number of latent factors. In the second step, these latent factors are forecast with an econometric model. The term structure of log-forces of mortality is next reconstructed by an inverse transformation. The neural analyzer is adjusted to French, UK and US mortality rates, over the period 1946–2000 and validated with data from 2001 to 2014. Numerical experiments reveal that the neural approach has an excellent predictive power, compared to the Lee–Carter model with and without cohort effects.

Suggested Citation

  • Hainaut, Donatien, 2018. "A Neural-Network Analyzer For Mortality Forecast," ASTIN Bulletin, Cambridge University Press, vol. 48(2), pages 481-508, May.
    • Handle: RePEc:cup:astinb:v:48:y:2018:i:02:p:481-508_00
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    Cited by:

    1. Zan Yu & Lianzeng Zhang, 2024. "Computing the Gerber-Shiu function with interest and a constant dividend barrier by physics-informed neural networks," Papers 2401.04378, arXiv.org.
    2. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    3. Andrea Nigri & Susanna Levantesi & Jose Manuel Aburto, 2022. "Leveraging deep neural networks to estimate age-specific mortality from life expectancy at birth," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 47(8), pages 199-232.
    4. Miguel Santolino, 2023. "Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?," Risks, MDPI, vol. 11(10), pages 1-21, September.
    5. Susanna Levantesi & Virginia Pizzorusso, 2019. "Application of Machine Learning to Mortality Modeling and Forecasting," Risks, MDPI, vol. 7(1), pages 1-19, February.
    6. G'abor Petneh'azi & J'ozsef G'all, 2019. "Mortality rate forecasting: can recurrent neural networks beat the Lee-Carter model?," Papers 1909.05501, arXiv.org, revised Oct 2019.
    7. Jin, Zhuo & Yang, Hailiang & Yin, G., 2021. "A hybrid deep learning method for optimal insurance strategies: Algorithms and convergence analysis," Insurance: Mathematics and Economics, Elsevier, vol. 96(C), pages 262-275.
    8. Francesca Perla & Salvatore Scognamiglio, 2023. "Locally-coherent multi-population mortality modelling via neural networks," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 46(1), pages 157-176, June.
    9. Jose Garrido & Yuxiang Shang & Ran Xu, 2024. "LSTM-Based Coherent Mortality Forecasting for Developing Countries," Risks, MDPI, vol. 12(2), pages 1-24, February.
    10. Alexandre Boumezoued & Amal Elfassihi, 2020. "Mortality data correction in the absence of monthly fertility records," Working Papers hal-02634631, HAL.
    11. 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.
    12. Boumezoued, Alexandre & Elfassihi, Amal, 2021. "Mortality data correction in the absence of monthly fertility records," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 486-508.
    13. Joab Odhiambo & Patrick Weke & Philip Ngare, 2021. "A Deep Learning Integrated Cairns-Blake-Dowd (CBD) Sytematic Mortality Risk Model," JRFM, MDPI, vol. 14(6), pages 1-12, June.

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