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Estimating the term structure of mortality

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  • Hári, Norbert
  • De Waegenaere, Anja
  • Melenberg, Bertrand
  • Nijman, Theo E.

Abstract

In modeling and forecasting mortality the Lee-Carter approach is the benchmark methodology. In many empirical applications the Lee-Carter approach results in a model that describes the log central death rates by means of linear trends. However, due to the volatility in (past) mortality data, the estimation of these trends, and, thus, the forecasts based on them, might be rather sensitive to the sample period employed. We allow for time-varying trends, depending on a few underlying factors, to make the estimates of the future trends less sensitive to the sampling period. We formulate our model in a state-space framework, and use the Kalman filtering technique to estimate it. We illustrate our model using Dutch mortality data.

Suggested Citation

  • Hári, Norbert & De Waegenaere, Anja & Melenberg, Bertrand & Nijman, Theo E., 2008. "Estimating the term structure of mortality," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 492-504, April.
  • Handle: RePEc:eee:insuma:v:42:y:2008:i:2:p:492-504
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    References listed on IDEAS

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    1. Renshaw, A. E. & Haberman, S., 2003. "On the forecasting of mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 32(3), pages 379-401, July.
    2. Renshaw, A.E. & Haberman, S. & Hatzopoulos, P., 1996. "The Modelling of Recent Mortality Trends in United Kingdom Male Assured Lives," British Actuarial Journal, Cambridge University Press, vol. 2(02), pages 449-477, June.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178.
    4. Carter, Lawrence R. & Lee, Ronald D., 1992. "Modeling and forecasting US sex differentials in mortality," International Journal of Forecasting, Elsevier, vol. 8(3), pages 393-411, November.
    5. Brouhns, Natacha & Denuit, Michel & Vermunt, Jeroen K., 2002. "A Poisson log-bilinear regression approach to the construction of projected lifetables," Insurance: Mathematics and Economics, Elsevier, vol. 31(3), pages 373-393, December.
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    Citations

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    Cited by:

    1. Katja Hanewald, 2009. "Mortality modeling: Lee-Carter and the macroeconomy," SFB 649 Discussion Papers SFB649DP2009-008, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    2. Katja Hanewald & Thomas Post & Helmut Gründl, 2011. "Stochastic Mortality, Macroeconomic Risks and Life Insurer Solvency," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 36(3), pages 458-475, July.
    3. Anja De Waegenaere & Bertrand Melenberg & Ralph Stevens, 2010. "Longevity Risk," De Economist, Springer, vol. 158(2), pages 151-192, June.
    4. Cox, Samuel H. & Lin, Yijia & Pedersen, Hal, 2010. "Mortality risk modeling: Applications to insurance securitization," Insurance: Mathematics and Economics, Elsevier, vol. 46(1), pages 242-253, February.
    5. Lin, Tzuling & Tsai, Cary Chi-Liang, 2013. "On the mortality/longevity risk hedging with mortality immunization," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 580-596.
    6. David Blake & Andrew Cairns & Guy Coughlan & Kevin Dowd & Richard MacMinn, 2013. "The New Life Market," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 501-558, September.
    7. O’Hare, Colin & Li, Youwei, 2012. "Explaining young mortality," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 12-25.
    8. O'Hare, Colin & Li, Youwei, 2014. "Is mortality spatial or social?," Economic Modelling, Elsevier, vol. 42(C), pages 198-207.
    9. Blake, David & Brockett, Patrick & Cox, Samuel & MacMinn, Richard, 2011. "Longevity risk and capital markets: The 2009-2010 update," MPRA Paper 28868, University Library of Munich, Germany.

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