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Forecasting Mortality Trends Allowing for Cause-of-Death Mortality Dependence

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  • Séverine Arnold (-Gaille)
  • Michael Sherris

Abstract

Longevity risk is among the most important factors to consider for pricing and risk management of longevity products. Past improvements in mortality over many years, and the uncertainty of these improvements, have attracted the attention of experts, both practitioners and academics. Since aggregate mortality rates reflect underlying trends in causes of death, insurers and demographers are increasingly considering cause-of-death data to better understand risks in their mortality assumptions. The relative importance of causes of death has changed over many years. As one cause reduces, others increase or decrease. The dependence between mortality for different causes of death is important when projecting future mortality. However, for scenario analysis based on causes of death, the assumption usually made is that causes of death are independent. Recent models, in the form of Vector Error Correction Models (VECMs), have been developed for multivariate dynamic systems and capture time dependency with common stochastic trends. These models include long-run stationary relations between the variables and thus allow a better understanding of the nature of this dependence. This article applies VECMs to cause-of-death mortality rates to assess the dependence between these competing risks. We analyze the five main causes of death in Switzerland. Our analysis confirms the existence of a long-run stationary relationship between these five causes. This estimated relationship is then used to forecast mortality rates, which are shown to be an improvement over forecasts from more traditional ARIMA processes, which do not allow for cause-of-death dependencies.

Suggested Citation

  • Séverine Arnold (-Gaille) & Michael Sherris, 2013. "Forecasting Mortality Trends Allowing for Cause-of-Death Mortality Dependence," North American Actuarial Journal, Taylor & Francis Journals, vol. 17(4), pages 273-282.
  • Handle: RePEc:taf:uaajxx:v:17:y:2013:i:4:p:273-282
    DOI: 10.1080/10920277.2013.838141
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    Cited by:

    1. Søren Kjærgaard & Yunus Emre Ergemen & Malene Kallestrup-Lamb & Jim Oeppen & Rune Lindahl-Jacobsen, 2019. "Forecasting Causes of Death using Compositional Data Analysis: the Case of Cancer Deaths," CREATES Research Papers 2019-07, Department of Economics and Business Economics, Aarhus University.
    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. Zhang, Xuanming & Huang, Fei & Hui, Francis K.C. & Haberman, Steven, 2023. "Cause-of-death mortality forecasting using adaptive penalized tensor decompositions," Insurance: Mathematics and Economics, Elsevier, vol. 111(C), pages 193-213.
    4. Nhan Huynh & Mike Ludkovski, 2021. "Joint Models for Cause-of-Death Mortality in Multiple Populations," Papers 2111.06631, arXiv.org.
    5. Arnold, Séverine & Glushko, Viktoriya, 2021. "Cause-specific mortality rates: Common trends and differences," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 294-308.
    6. Graziani, Rebecca & NIGRI, ANDREA, 2023. "An Age–Period–Cohort Model in a Dirichlet Framework: A Coherent Causes of Death Estimation," SocArXiv 856yw, Center for Open Science.
    7. Hunt, Andrew & Blake, David, 2015. "Modelling longevity bonds: Analysing the Swiss Re Kortis bond," Insurance: Mathematics and Economics, Elsevier, vol. 63(C), pages 12-29.
    8. Li, Han & Li, Hong & Lu, Yang & Panagiotelis, Anastasios, 2019. "A forecast reconciliation approach to cause-of-death mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 86(C), pages 122-133.
    9. Camille Delbrouck & Jennifer Alonso-García, 2024. "COVID-19 and Excess Mortality: An Actuarial Study," Risks, MDPI, vol. 12(4), pages 1-27, March.
    10. Valeria D’Amato & Emilia Di Lorenzo & Marilena Sibillo, 2018. "Dread Disease and Cause-Specific Mortality: Exploring New Forms of Insured Loans," Risks, MDPI, vol. 6(1), pages 1-21, February.

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