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Selecting stochastic mortality models for the Italian population

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  • Paola Biffi
  • Gian Clemente

Abstract

The future revision of capital requirements and a market-consistent valuation of non-hedgeable liabilities lead to an increasing attention on forecasting longevity trends. In this field, many methodologies focus on either modeling mortality or pricing mortality-linked securities (as longevity bonds). Following Lee–Carter method (proposed in 1992), actuarial literature has provided several extensions in order to consider different trends observed in European data set (e.g., the cohort effect). The purpose of the paper is to compare the features of main mortality models proposed over the years. Model selection became indeed a primary task with the aim to identify the “best” model. What is meant by best is controversial, but good selection techniques are usually based on a good balance between goodness of fit and simplicity. In this regard, different criteria, mainly based on residual and projected rates analysis, are here used. For the sake of comparison, main forecasting methods have been applied to deaths and exposures to risk of male Italian population. Weaknesses and strengths have been emphasized, by underlying how various models provide a different goodness of fit according to different data sets. At the same time, the quality and the variability of forecasted rates have been compared by evaluating the effect on annuity values. Results confirm that some models perform better than others, but no single model can be defined as the best method. Copyright Springer-Verlag 2014

Suggested Citation

  • Paola Biffi & Gian Clemente, 2014. "Selecting stochastic mortality models for the Italian population," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 37(2), pages 255-286, October.
  • Handle: RePEc:spr:decfin:v:37:y:2014:i:2:p:255-286
    DOI: 10.1007/s10203-012-0131-9
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    References listed on IDEAS

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    1. David Blake & Andrew Cairns & Kevin Dowd & Richard MacMinn, 2006. "Longevity Bonds: Financial Engineering, Valuation, and Hedging," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 647-672, December.
    2. Andrew Cairns & David Blake & Kevin Dowd & Guy Coughlan & David Epstein & Alen Ong & Igor Balevich, 2009. "A Quantitative Comparison of Stochastic Mortality Models Using Data From England and Wales and the United States," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(1), pages 1-35.
    3. Willets, R. C., 2004. "The Cohort Effect: Insights and Explanations," British Actuarial Journal, Cambridge University Press, vol. 10(4), pages 833-877, October.
    4. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    5. Dahl, Mikkel, 2004. "Stochastic mortality in life insurance: market reserves and mortality-linked insurance contracts," Insurance: Mathematics and Economics, Elsevier, vol. 35(1), pages 113-136, August.
    6. Renshaw, A.E. & Haberman, S., 2006. "A cohort-based extension to the Lee-Carter model for mortality reduction factors," Insurance: Mathematics and Economics, Elsevier, vol. 38(3), pages 556-570, June.
    7. 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.
    8. Haberman, Steven & Renshaw, Arthur, 2009. "On age-period-cohort parametric mortality rate projections," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 255-270, October.
    9. Biffis, Enrico, 2005. "Affine processes for dynamic mortality and actuarial valuations," Insurance: Mathematics and Economics, Elsevier, vol. 37(3), pages 443-468, December.
    10. Ronald Lee, 2000. "The Lee-Carter Method for Forecasting Mortality, with Various Extensions and Applications," North American Actuarial Journal, Taylor & Francis Journals, vol. 4(1), pages 80-91.
    11. Andrew J. G. Cairns & David Blake & Kevin Dowd, 2006. "A Two‐Factor Model for Stochastic Mortality with Parameter Uncertainty: Theory and Calibration," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 73(4), pages 687-718, December.
    12. Kevin Dowd & Andrew Cairns & David Blake & Guy Coughlan & David Epstein & Marwa Khalaf-Allah, 2010. "Backtesting Stochastic Mortality Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 14(3), pages 281-298.
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    Cited by:

    1. Salvatore Scognamiglio & Mario Marino, 2023. "Backtesting stochastic mortality models by prediction interval-based metrics," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(4), pages 3825-3847, August.
    2. David Atance & Alejandro Balbás & Eliseo Navarro, 2020. "Constructing dynamic life tables with a single-factor model," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 43(2), pages 787-825, December.
    3. Benedetta Frassi & Fabio Pammolli & Luca Regis, 2017. "The potential costs of Longevity Risk on Public Pensions. Evidence from Italian data," Working Papers 01/2017, IMT School for Advanced Studies Lucca, revised Jan 2017.

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    More about this item

    Keywords

    C02 - Mathematical methods; C52 - Model evaluation; validation and selection; G22 - Insurance; Insurance companies; Projected mortality models; Poisson distribution; Age-period and cohort effect on mortality; Bayesian information criterion; Residual analysis;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies

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