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A Quantitative Comparison of the Lee-Carter Model under Different Types of Non-Gaussian Innovations

Listed author(s):
  • Chou-Wen Wang

    (Department of Risk Management and Insurance, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan)

  • Hong-Chih Huang

    (Department of Risk Management and Insurance, Research Fellow of Risk and Insurance Research Center, National Chengchi University, Taipei, Taiwan)

  • I-Chien Liu

    (Department of Risk Management and Insurance, National Chengchi University, Taipei, Taiwan)

Registered author(s):

    In the classical Lee-Carter model, the mortality indices that are assumed to be a random walk model with drift are normally distributed. However, for the long-term mortality data, the error terms of the Lee-Carter model and the mortality indices have tails thicker than those of a normal distribution and appear to be skewed. This study therefore adopts five non-Gaussian distributions—Student’s t-distribution and its skew extension (i.e., generalised hyperbolic skew Student’s t-distribution), one finite-activity Lévy model (jump diffusion distribution), and two infinite-activity or pure jump models (variance gamma and normal inverse Gaussian)—to model the error terms of the Lee-Carter model. With mortality data from six countries over the period 1900–2007, both in-sample model selection criteria (e.g., Bayesian information criterion, Kolmogorov–Smirnov test, Anderson–Darling test, Cramér–von-Mises test) and out-of-sample projection errors indicate a preference for modelling the Lee-Carter model with non-Gaussian innovations.

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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: 675-696

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    Handle: RePEc:pal:gpprii:v:36:y:2011:i:4:p:675-696
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