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A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19

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  • Maria Francesca Carfora

    (Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via P. Castellino, 111, 80131 Naples, Italy)

  • Albina Orlando

    (Istituto per le Applicazioni del Calcolo “Mauro Picone”, Consiglio Nazionale delle Ricerche, Via P. Castellino, 111, 80131 Naples, Italy)

Abstract

Mortality shocks, such as pandemics, threaten the consolidated longevity improvements, confirmed in the last decades for the majority of western countries. Indeed, just before the COVID-19 pandemic, mortality was falling for all ages, with a different behavior according to different ages and countries. It is indubitable that the changes in the population longevity induced by shock events, even transitory ones, affecting demographic projections, have financial implications in public spending as well as in pension plans and life insurance. The Short Term Mortality Fluctuations (STMF) data series, providing data of all-cause mortality fluctuations by week within each calendar year for 38 countries worldwide, offers a powerful tool to timely analyze the effects of the mortality shock caused by the COVID-19 pandemic on Italian mortality rates. This dataset, recently made available as a new component of the Human Mortality Database, is described and techniques for the integration of its data with the historical mortality time series are proposed. Then, to forecast mortality rates, the well-known stochastic mortality model proposed by Lee and Carter in 1992 is first considered, to be consistent with the internal processing of the Human Mortality Database, where exposures are estimated by the Lee–Carter model; empirical results are discussed both on the estimation of the model coefficients and on the forecast of the mortality rates. In detail, we show how the integration of the yearly aggregated STMF data in the HMD database allows the Lee–Carter model to capture the complex evolution of the Italian mortality rates, including the higher lethality for males and older people, in the years that follow a large shock event such as the COVID-19 pandemic. Finally, we discuss some key points concerning the improvement of existing models to take into account mortality shocks and evaluate their impact on future mortality dynamics.

Suggested Citation

  • Maria Francesca Carfora & Albina Orlando, 2023. "A Preliminary Investigation of a Single Shock Impact on Italian Mortality Rates Using STMF Data: A Case Study of COVID-19," Data, MDPI, vol. 8(6), pages 1-12, June.
    • Handle: RePEc:gam:jdataj:v:8:y:2023:i:6:p:107-:d:1170369
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    References listed on IDEAS

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    1. Hua Chen & Samuel H. Cox, 2009. "Modeling Mortality With Jumps: Applications to Mortality Securitization," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 76(3), pages 727-751, September.
    2. Zhou, Rui & Li, Johnny Siu-Hang, 2022. "A multi-parameter-level model for simulating future mortality scenarios with COVID-alike effects," Annals of Actuarial Science, Cambridge University Press, vol. 16(3), pages 453-477, November.
    3. Liu, Yanxin & Li, Johnny Siu-Hang, 2015. "The age pattern of transitory mortality jumps and its impact on the pricing of catastrophic mortality bonds," Insurance: Mathematics and Economics, Elsevier, vol. 64(C), pages 135-150.
    4. Jens Robben & Katrien Antonio & Sander Devriendt, 2022. "Assessing the Impact of the COVID-19 Shock on a Stochastic Multi-Population Mortality Model," Risks, MDPI, vol. 10(2), pages 1-33, January.
    5. Marília R. Nepomuceno & Ilya Klimkin & Dmitri A. Jdanov & Ainhoa Alustiza‐Galarza & Vladimir M. Shkolnikov, 2022. "Sensitivity Analysis of Excess Mortality due to the COVID‐19 Pandemic," Population and Development Review, The Population Council, Inc., vol. 48(2), pages 279-302, June.
    6. Schnürch, Simon & Kleinow, Torsten & Korn, Ralf & Wagner, Andreas, 2022. "The impact of mortality shocks on modelling and insurance valuation as exemplified by COVID-19," Annals of Actuarial Science, Cambridge University Press, vol. 16(3), pages 498-526, November.
    7. Carfora, M.F. & Cutillo, L. & Orlando, A., 2017. "A quantitative comparison of stochastic mortality models on Italian population data," Computational Statistics & Data Analysis, Elsevier, vol. 112(C), pages 198-214.
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