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Modelling Mortality with Common Stochastic Long-Run Trends

Listed author(s):
  • Séverine Gaille


    (Faculty of Business and Economics, Department of Actuarial Science, University of Lausanne, 1015 Lausanne, Switzerland.)

  • Michael Sherris


    (Australian School of Business, School of Actuarial Studies, University of New South Wales, Sydney NSW 2052, Australia.)

Registered author(s):

    Modelling mortality and longevity risk is critical to assessing risk for insurers issuing longevity risk products. It has challenged practitioners and academics alike because of first the existence of common stochastic trends and second the unpredictability of an eventual mortality improvement in some age groups. When considering cause-of-death mortality rates, both aforementioned trends are additionally affected by the cause of death. Longevity trends are usually forecasted using a Lee-Carter model with a single stochastic time series for period improvements, or using an age-based parametric model with univariate time series for the parameters. We assess a multivariate time series model for the parameters of the Heligman-Pollard function, through Vector Error Correction Models which include the common stochastic long-run trends. The model is applied to circulatory disease deaths in U.S. over a 50-year period and is shown to be an improvement over both the Lee-Carter model and the stochastic parameter ARIMA Heligman-Pollard model.

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    Article provided by Palgrave Macmillan & The Geneva Association in its journal The Geneva Papers on Risk and Insurance Issues and Practice.

    Volume (Year): 36 (2011)
    Issue (Month): 4 (October)
    Pages: 595-621

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    Handle: RePEc:pal:gpprii:v:36:y:2011:i:4:p:595-621
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