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Optimal retirement consumption with a stochastic force of mortality

Author

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  • Huaxiong Huang
  • Moshe A. Milevsky
  • Thomas S. Salisbury

Abstract

We extend the lifecycle model (LCM) of consumption over a random horizon (a.k.a. the Yaari model) to a world in which (i.) the force of mortality obeys a diffusion process as opposed to being deterministic, and (ii.) a consumer can adapt their consumption strategy to new information about their mortality rate (a.k.a. health status) as it becomes available. In particular, we derive the optimal consumption rate and focus on the impact of mortality rate uncertainty vs. simple lifetime uncertainty -- assuming the actuarial survival curves are initially identical -- in the retirement phase where this risk plays a greater role. In addition to deriving and numerically solving the PDE for the optimal consumption rate, our main general result is that when utility preferences are logarithmic the initial consumption rates are identical. But, in a CRRA framework in which the coefficient of relative risk aversion is greater (smaller) than one, the consumption rate is higher (lower) and a stochastic force of mortality does make a difference. That said, numerical experiments indicate that even for non-logarithmic preferences, the stochastic mortality effect is relatively minor from the individual's perspective. Our results should be relevant to researchers interested in calibrating the lifecycle model as well as those who provide normative guidance (a.k.a. financial advice) to retirees.

Suggested Citation

  • Huaxiong Huang & Moshe A. Milevsky & Thomas S. Salisbury, 2012. "Optimal retirement consumption with a stochastic force of mortality," Papers 1205.2295, arXiv.org.
  • Handle: RePEc:arx:papers:1205.2295
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Paweł Rokita & Radosław Pietrzyk & Łukasz Feldman, 2014. "Multiobjective Optimization of Financing Household Goals with Multiple Investment Programs," Statistics in Transition new series, Główny Urząd Statystyczny (Polska), vol. 15(2), pages 243-268, March.
    2. Ewald, Christian-Oliver & Zhang, Aihua, 2017. "On the effects of changing mortality patterns on investment, labour and consumption under uncertainty," Insurance: Mathematics and Economics, Elsevier, vol. 73(C), pages 105-115.
    3. Feigenbaum, James, 2016. "Equivalent representations of non-exponential discounting models," Journal of Mathematical Economics, Elsevier, vol. 66(C), pages 58-71.
    4. Post, Thomas & Hanewald, Katja, 2013. "Longevity risk, subjective survival expectations, and individual saving behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 86(C), pages 200-220.
    5. Francesco Menoncin & Luca Regis, 2015. "Longevity assets and pre-retirement consumption/portfolio decisions," Working Papers 2/2015, IMT Institute for Advanced Studies Lucca, revised May 2015.
    6. repec:eee:dyncon:v:84:y:2017:i:c:p:58-76 is not listed on IDEAS
    7. repec:eee:insuma:v:76:y:2017:i:c:p:75-86 is not listed on IDEAS
    8. repec:bla:jrinsu:v:84:y:2017:i:4:p:1171-1202 is not listed on IDEAS
    9. Schendel, Lorenz S., 2014. "Consumption-investment problems with stochastic mortality risk," SAFE Working Paper Series 43, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.
    10. repec:eee:insuma:v:78:y:2018:i:c:p:157-173 is not listed on IDEAS
    11. Delong, Łukasz & Chen, An, 2016. "Asset allocation, sustainable withdrawal, longevity risk and non-exponential discounting," Insurance: Mathematics and Economics, Elsevier, vol. 71(C), pages 342-352.
    12. de Kort, J. & Vellekoop, M.H., 2017. "Existence of optimal consumption strategies in markets with longevity risk," Insurance: Mathematics and Economics, Elsevier, vol. 72(C), pages 107-121.
    13. Kraft, Holger & Schendel, Lorenz S. & Steffensen, Mogens, 2014. "Life insurance demand under health shock risk," SAFE Working Paper Series 40, Research Center SAFE - Sustainable Architecture for Finance in Europe, Goethe University Frankfurt.

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