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Heterogeneity of Australian population mortality and implications for a viable life annuity market

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  • Su, Shu
  • Sherris, Michael

Abstract

Heterogeneity in mortality rates is known to exist in populations, undermining the use of age and sex as the only rating factors for life insurance and annuity products. Life insurers offering life annuities assume that annuitant lives will self-select, and price the longevity risk with an annuity mortality table that assumes above-average longevity. This leads to annuities being less attractive to a wide range of individuals, and limits the viability of private annuity markets. This paper quantifies heterogeneity and its financial implications for annuity prices using well-established frailty models and a more recently developed Markov ageing model calibrated to population mortality data. The heterogeneity implied for each model is quantified using Australian population mortality. The models are compared by considering the distribution of heterogeneity by age implied by the models and the implications for life annuity prices.

Suggested Citation

  • Su, Shu & Sherris, Michael, 2012. "Heterogeneity of Australian population mortality and implications for a viable life annuity market," Insurance: Mathematics and Economics, Elsevier, vol. 51(2), pages 322-332.
    • Handle: RePEc:eee:insuma:v:51:y:2012:i:2:p:322-332
    DOI: 10.1016/j.insmatheco.2012.05.006
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    References listed on IDEAS

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    1. Daniel Alai & Michael Sherris, 2012. "Rethinking Age-Period-Cohort Mortality Trend Models," Working Papers 201212, ARC Centre of Excellence in Population Ageing Research (CEPAR), Australian School of Business, University of New South Wales.
    2. X. Lin & Xiaoming Liu, 2007. "Markov Aging Process and Phase-Type Law of Mortality," North American Actuarial Journal, Taylor & Francis Journals, vol. 11(4), pages 92-109.
    3. James Vaupel & Kenneth Manton & Eric Stallard, 1979. "The impact of heterogeneity in individual frailty on the dynamics of mortality," Demography, Springer;Population Association of America (PAA), vol. 16(3), pages 439-454, August.
    4. Chris Elbers & Geert Ridder, 1982. "True and Spurious Duration Dependence: The Identifiability of the Proportional Hazard Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 49(3), pages 403-409.
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    Citations

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    Cited by:

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    2. Bruszas, Sandy & Kaschützke, Barbara & Maurer, Raimond & Siegelin, Ivonne, 2018. "Unisex pricing of German participating life annuities—Boon or bane for customer and insurance company?," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 230-245.
    3. Jinhui Zhang & Sachi Purcal & Jiaqin Wei, 2017. "Optimal Time to Enter a Retirement Village," Risks, MDPI, vol. 5(1), pages 1-20, March.
    4. Milevsky, Moshe A., 2020. "Calibrating Gompertz in reverse: What is your longevity-risk-adjusted global age?," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 147-161.
    5. Meyricke, Ramona & Sherris, Michael, 2013. "The determinants of mortality heterogeneity and implications for pricing annuities," Insurance: Mathematics and Economics, Elsevier, vol. 53(2), pages 379-387.
    6. Govorun, Maria & Latouche, Guy & Loisel, Stéphane, 2015. "Phase-type aging modeling for health dependent costs," Insurance: Mathematics and Economics, Elsevier, vol. 62(C), pages 173-183.
    7. Annamaria Olivieri & Ermanno Pitacco, 2016. "Frailty and Risk Classification for Life Annuity Portfolios," Risks, MDPI, vol. 4(4), pages 1-23, October.
    8. Franck Adékambi, 2019. "Moments Of Phase-Type Aging Modeling For Health Dependent Costs," Advances in Decision Sciences, Asia University, Taiwan, vol. 23(2), pages 37-64, June.
    9. Rafal Chomik & John Piggott, 2016. "Australian Superannuation: The Current State of Play," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 49(4), pages 483-493, December.
    10. Boquan Cheng & Rogemar Mamon, 2023. "A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 142-187, January.

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    More about this item

    Keywords

    Longevity risk; Mortality heterogeneity; Frailty model; Markov ageing model; Physiological age; Annuity pricing;
    All these keywords.

    JEL classification:

    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts
    • C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions

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