IDEAS home Printed from https://ideas.repec.org/a/eee/insuma/v50y2012i1p12-25.html
   My bibliography  Save this article

Explaining young mortality

Author

Listed:
  • O’Hare, Colin
  • Li, Youwei

Abstract

Stochastic modeling of mortality rates focuses on fitting linear models to logarithmically adjusted mortality data from the middle or late ages. Whilst this modeling enables insurers to project mortality rates and hence price mortality products it does not provide good fit for younger aged mortality. Mortality rates below the early 20’s are important to model as they give an insight into estimates of the cohort effect for more recent years of birth. It is also important given the cumulative nature of life expectancy to be able to forecast mortality improvements at all ages. When we attempt to fit existing models to a wider age range, 5–89, rather than 20–89 or 50–89, their weaknesses are revealed as the results are not satisfactory. The linear innovations in existing models are not flexible enough to capture the non-linear profile of mortality rates that we see at the lower ages. In this paper, we modify an existing 4 factor model of mortality to enable better fitting to a wider age range, and using data from seven developed countries our empirical results show that the proposed model has a better fit to the actual data, is robust, and has good forecasting ability.

Suggested Citation

  • O’Hare, Colin & Li, Youwei, 2012. "Explaining young mortality," Insurance: Mathematics and Economics, Elsevier, vol. 50(1), pages 12-25.
  • Handle: RePEc:eee:insuma:v:50:y:2012:i:1:p:12-25
    DOI: 10.1016/j.insmatheco.2011.09.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167668711000990
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.insmatheco.2011.09.005?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
    2. Ronald Lee & Timothy Miller, 2001. "Evaluating the performance of the lee-carter method for forecasting mortality," Demography, Springer;Population Association of America (PAA), vol. 38(4), pages 537-549, November.
    3. 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.
    4. Plat, Richard, 2009. "On stochastic mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 393-404, December.
    5. 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.
    6. 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.
    7. 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.
    8. Robert Hauser & David Weir, 2010. "Recent developments in longitudinal studies of aging in the United States," Demography, Springer;Population Association of America (PAA), vol. 47(1), pages 111-130, March.
    9. Dowd, Kevin & Cairns, Andrew J.G. & Blake, David & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2010. "Evaluating the goodness of fit of stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 47(3), pages 255-265, December.
    10. 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.
    11. Renshaw, A. E. & Haberman, S., 2003. "Lee-Carter mortality forecasting with age-specific enhancement," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 255-272, October.
    12. 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.
    13. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kung, Ko-Lun & MacMinn, Richard D. & Kuo, Weiyu & Tsai, Chenghsien Jason, 2022. "Multi-population mortality modeling: When the data is too much and not enough," Insurance: Mathematics and Economics, Elsevier, vol. 103(C), pages 41-55.
    2. Li, Han & O’Hare, Colin, 2017. "Semi-parametric extensions of the Cairns–Blake–Dowd model: A one-dimensional kernel smoothing approach," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 166-176.
    3. Niu, G., 2014. "Essays on subjective expectations and mortality trends," Other publications TiSEM b9f72836-d8ad-478b-adca-4, Tilburg University, School of Economics and Management.
    4. Li, Han & Hyndman, Rob J., 2021. "Assessing mortality inequality in the U.S.: What can be said about the future?," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 152-162.
    5. O'Hare, Colin & Li, Youwei, 2014. "Is mortality spatial or social?," Economic Modelling, Elsevier, vol. 42(C), pages 198-207.
    6. Lydia Dutton & Athanasios A. Pantelous & Malgorzata Seklecka, 2020. "The impact of economic growth in mortality modelling for selected OECD countries," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(3), pages 533-550, April.
    7. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    8. Colin O’hare & Youwei Li, 2017. "Models of mortality rates – analysing the residuals," Applied Economics, Taylor & Francis Journals, vol. 49(52), pages 5309-5323, November.
    9. Bullough, Steve & Davies, Larissa E. & Barrett, David, 2015. "The impact of a community free swimming programme for young people (under 19) in England," Sport Management Review, Elsevier, vol. 18(1), pages 32-44.
    10. Geng Niu & Bertrand Melenberg, 2014. "Trends in Mortality Decrease and Economic Growth," Demography, Springer;Population Association of America (PAA), vol. 51(5), pages 1755-1773, October.
    11. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    12. Colin O’hare & Youwei Li, 2017. "Modelling mortality: are we heading in the right direction?," Applied Economics, Taylor & Francis Journals, vol. 49(2), pages 170-187, January.
    13. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.
    14. Anastasia Novokreshchenova, 2016. "Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models," Risks, MDPI, vol. 4(4), pages 1-28, December.
    15. Han Li & Colin O’Hare, 2019. "Mortality Forecasting: How Far Back Should We Look in Time?," Risks, MDPI, vol. 7(1), pages 1-15, February.
    16. Li, Han & O’Hare, Colin & Zhang, Xibin, 2015. "A semiparametric panel approach to mortality modeling," Insurance: Mathematics and Economics, Elsevier, vol. 61(C), pages 264-270.
    17. Wang, Hong & Koo, Bonsoo & O'Hare, Colin, 2016. "Retirement planning in the light of changing demographics," Economic Modelling, Elsevier, vol. 52(PB), pages 749-763.
    18. Kung, Ko-Lun & Liu, I-Chien & Wang, Chou-Wen, 2021. "Modeling and pricing longevity derivatives using Skellam distribution," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 341-354.
    19. O'Hare, Colin & Li, Youwei, 2014. "Identifying structural breaks in stochastic mortality models," MPRA Paper 62994, University Library of Munich, Germany.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Blake, David & El Karoui, Nicole & Loisel, Stéphane & MacMinn, Richard, 2018. "Longevity risk and capital markets: The 2015–16 update," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 157-173.
    2. Blake, David & Cairns, Andrew J.G., 2021. "Longevity risk and capital markets: The 2019-20 update," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 395-439.
    3. Colin O’hare & Youwei Li, 2017. "Models of mortality rates – analysing the residuals," Applied Economics, Taylor & Francis Journals, vol. 49(52), pages 5309-5323, November.
    4. Jaap Spreeuw & Iqbal Owadally & Muhammad Kashif, 2022. "Projecting Mortality Rates Using a Markov Chain," Mathematics, MDPI, vol. 10(7), pages 1-18, April.
    5. Basellini, Ugofilippo & Camarda, Carlo Giovanni & Booth, Heather, 2023. "Thirty years on: A review of the Lee–Carter method for forecasting mortality," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1033-1049.
    6. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2016. "A unified approach to mortality modelling using state-space framework: characterisation, identification, estimation and forecasting," Papers 1605.09484, arXiv.org.
    7. Colin O’hare & Youwei Li, 2017. "Modelling mortality: are we heading in the right direction?," Applied Economics, Taylor & Francis Journals, vol. 49(2), pages 170-187, January.
    8. Apostolos Bozikas & Georgios Pitselis, 2018. "An Empirical Study on Stochastic Mortality Modelling under the Age-Period-Cohort Framework: The Case of Greece with Applications to Insurance Pricing," Risks, MDPI, vol. 6(2), pages 1-34, April.
    9. O'Hare, Colin & Li, Youwei, 2014. "Identifying structural breaks in stochastic mortality models," MPRA Paper 62994, University Library of Munich, Germany.
    10. Li, Han & O’Hare, Colin, 2017. "Semi-parametric extensions of the Cairns–Blake–Dowd model: A one-dimensional kernel smoothing approach," Insurance: Mathematics and Economics, Elsevier, vol. 77(C), pages 166-176.
    11. James Risk & Michael Ludkovski, 2015. "Statistical Emulators for Pricing and Hedging Longevity Risk Products," Papers 1508.00310, arXiv.org, revised Sep 2015.
    12. Rachel WINGENBACH & Jong-Min KIM & Hojin JUNG, 2020. "Living Longer in High Longevity Risk," JODE - Journal of Demographic Economics, Cambridge University Press, vol. 86(1), pages 47-86, March.
    13. Cairns, Andrew J.G. & Blake, David & Dowd, Kevin & Coughlan, Guy D. & Epstein, David & Khalaf-Allah, Marwa, 2011. "Mortality density forecasts: An analysis of six stochastic mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 48(3), pages 355-367, May.
    14. Norkhairunnisa Redzwan & Rozita Ramli, 2022. "A Bibliometric Analysis of Research on Stochastic Mortality Modelling and Forecasting," Risks, MDPI, vol. 10(10), pages 1-17, October.
    15. Man Chung Fung & Gareth W. Peters & Pavel V. Shevchenko, 2017. "Cohort effects in mortality modelling: a Bayesian state-space approach," Papers 1703.08282, arXiv.org.
    16. David Atance & Ana Debón & Eliseo Navarro, 2020. "A Comparison of Forecasting Mortality Models Using Resampling Methods," Mathematics, MDPI, vol. 8(9), pages 1-21, September.
    17. Risk, J. & Ludkovski, M., 2016. "Statistical emulators for pricing and hedging longevity risk products," Insurance: Mathematics and Economics, Elsevier, vol. 68(C), pages 45-60.
    18. Ahmadi, Seyed Saeed & Li, Johnny Siu-Hang, 2014. "Coherent mortality forecasting with generalized linear models: A modified time-transformation approach," Insurance: Mathematics and Economics, Elsevier, vol. 59(C), pages 194-221.
    19. Anastasia Novokreshchenova, 2016. "Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models," Risks, MDPI, vol. 4(4), pages 1-28, December.
    20. Bravo, Jorge M. & Ayuso, Mercedes & Holzmann, Robert & Palmer, Edward, 2021. "Addressing the life expectancy gap in pension policy," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 200-221.

    More about this item

    Keywords

    Mortality; Stochastic models; Forecasting; Non-linearity;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G22 - Financial Economics - - Financial Institutions and Services - - - Insurance; Insurance Companies; Actuarial Studies
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
    • J11 - Labor and Demographic Economics - - Demographic Economics - - - Demographic Trends, Macroeconomic Effects, and Forecasts

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:insuma:v:50:y:2012:i:1:p:12-25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/505554 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.