IDEAS home Printed from https://ideas.repec.org/a/cup/anacsi/v16y2022i3p453-477_4.html
   My bibliography  Save this article

A multi-parameter-level model for simulating future mortality scenarios with COVID-alike effects

Author

Listed:
  • Zhou, Rui
  • Li, Johnny Siu-Hang

Abstract

There has been a growing interest among pension plan sponsors in envisioning how the mortality experience of their active and deferred members may turn out to be if a pandemic similar to the COVID-19 occurs in the future. To address their needs, we propose in this paper a stochastic model for simulating future mortality scenarios with COVID-alike effects. The proposed model encompasses three parameter levels. The first level includes parameters that capture the long-term pattern of mortality, whereas the second level contains parameters that gauge the excess age-specific mortality due to COVID-19. Parameters in the first and second levels are estimated using penalised quasi-likelihood maximisation method which was proposed for generalised linear mixed models. Finally, the third level includes parameters that draw on expert opinions concerning, for example, how likely a COVID-alike pandemic will occur in the future. We illustrate our proposed model with data from the United States and a range of expert opinions.

Suggested Citation

  • 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.
    • Handle: RePEc:cup:anacsi:v:16:y:2022:i:3:p:453-477_4
    as

    Download full text from publisher

    File URL: https://www.cambridge.org/core/product/identifier/S1748499522000033/type/journal_article
    File Function: link to article abstract page
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cup:anacsi:v:16:y:2022:i:3:p:453-477_4. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Kirk Stebbing (email available below). General contact details of provider: https://www.cambridge.org/aas .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.