IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v11y2023i12p207-d1289443.html
   My bibliography  Save this article

Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing

Author

Listed:
  • Huijing Li

    (College of Finance, Nanjing Agricultural University, Nanjing 210095, China)

  • Rui Zhou

    (Department of Economics, The University of Melbourne, Melbourne, VIC 3010, Australia)

  • Min Ji

    (Department of Mathematics, Towson University, Towson, MD 21252, USA)

Abstract

Human mortality has been improving faster than expected over the past few decades. This unprecedented improvement has caused significant financial stress to pension plan sponsors and annuity providers. The widely recognized Lee–Carter model often assumes linearity in its period effect as an integral part of the model. Nevertheless, deviation from linearity has been observed in historical mortality data. In this paper, we investigate the applicability of four nonlinear time-series models: threshold autoregressive model, Markov switching model, structural change model, and generalized autoregressive conditional heteroskedasticity model for mortality data. By analyzing the mortality data from England and Wales and Italy spanning the years 1900 to 2019, we compare the goodness of fit and forecasting performance of the four nonlinear models. We then demonstrate the implications of nonlinearity in mortality modeling on the pricing of longevity bonds as a practical illustration of our findings.

Suggested Citation

  • Huijing Li & Rui Zhou & Min Ji, 2023. "Nonlinear Modeling of Mortality Data and Its Implications for Longevity Bond Pricing," Risks, MDPI, vol. 11(12), pages 1-25, November.
    • Handle: RePEc:gam:jrisks:v:11:y:2023:i:12:p:207-:d:1289443
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/11/12/207/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/11/12/207/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhou, Rui, 2019. "Modelling Mortality Dependence With Regime-Switching Copulas," ASTIN Bulletin, Cambridge University Press, vol. 49(2), pages 373-407, May.
    2. Hainaut, Donatien, 2012. "Multidimensional Lee–Carter model with switching mortality processes," Insurance: Mathematics and Economics, Elsevier, vol. 50(2), pages 236-246.
    3. Frank van Berkum & Katrien Antonio & Michel Vellekoop, 2016. "The impact of multiple structural changes on mortality predictions," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2016(7), pages 581-603, August.
    4. Andreas Milidonis & Yijia Lin & Samuel Cox, 2011. "Mortality Regimes and Pricing," North American Actuarial Journal, Taylor & Francis Journals, vol. 15(2), pages 266-289.
    5. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    6. Sweeting, P. J., 2011. "A Trend-Change Extension of the Cairns-Blake-Dowd Model," Annals of Actuarial Science, Cambridge University Press, vol. 5(2), pages 143-162, September.
    7. 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.
    Full references (including those not matched with items on IDEAS)

    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. Börger, Matthias & Schupp, Johannes, 2018. "Modeling trend processes in parametric mortality models," Insurance: Mathematics and Economics, Elsevier, vol. 78(C), pages 369-380.
    4. Börger, Matthias & Russ, Jochen & Schupp, Johannes, 2021. "It takes two: Why mortality trend modeling is more than modeling one mortality trend," Insurance: Mathematics and Economics, Elsevier, vol. 99(C), pages 222-232.
    5. 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.
    6. Chou-Wen Wang & Hong-Chih Huang & I-Chien Liu, 2013. "Mortality Modeling With Non-Gaussian Innovations and Applications to the Valuation of Longevity Swaps," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 80(3), pages 775-798, September.
    7. Chiu, Chien-Liang & Chang, Ting-Huan, 2009. "What proportion of renewable energy supplies is needed to initially mitigate CO2 emissions in OECD member countries?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1669-1674, August.
    8. Nicolas Canry & Julien Fouquau & Sébastien Lechevalier, 2011. "Sectoral Price Dynamics in Japan: A Threshold Approach," Economics Bulletin, AccessEcon, vol. 31(2), pages 1322-1335.
    9. Jean C. Kouam & Simplice Asongu, 2022. "The non-linear effects of fixed broadband on economic growth in Africa," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(5), pages 881-895, August.
    10. Khémiri, Wafa & Noubbigh, Hédi, 2020. "Size-threshold effect in debt-firm performance nexus in the sub-Saharan region: A Panel Smooth Transition Regression approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 76(C), pages 335-344.
    11. Simplice A. Asongu & Nicholas M. Odhiambo, 2020. "Insurance Policy Thresholds for Economic Growth in Africa," The European Journal of Development Research, Palgrave Macmillan;European Association of Development Research and Training Institutes (EADI), vol. 32(3), pages 672-689, July.
    12. Becker, Sascha S. & Nautz, Dieter, 2012. "Inflation, price dispersion and market integration through the lens of a monetary search model," European Economic Review, Elsevier, vol. 56(3), pages 624-634.
    13. Jingwen Lu & Lihua Dai, 2023. "Examining the Threshold Effect of Environmental Regulation: The Impact of Agricultural Product Trade Openness on Agricultural Carbon Emissions," Sustainability, MDPI, vol. 15(13), pages 1-21, June.
    14. Ostadzad, Ali Hossein, 2022. "Innovation and carbon emissions: Fixed-effects panel threshold model estimation for renewable energy," Renewable Energy, Elsevier, vol. 198(C), pages 602-617.
    15. Dorothée Charlier & Sondès Kahouli, 2018. "Fuel poverty and residential energy demand: how fuel-poor households react to energy price fluctuations," Post-Print halshs-01957771, HAL.
    16. Coudert, Virginie & Couharde, Cécile & Mignon, Valérie, 2015. "On the impact of volatility on the real exchange rate – terms of trade nexus: Revisiting commodity currencies," Journal of International Money and Finance, Elsevier, vol. 58(C), pages 110-127.
    17. Zheng, Yingmei & Qi, Jianhong & Chen, Xiaoliang, 2011. "The effect of increasing exports on industrial energy intensity in China," Energy Policy, Elsevier, vol. 39(5), pages 2688-2698, May.
    18. Kelbesa Megersa & Danny Cassimon, 2015. "Public Debt, Economic Growth, and Public Sector Management in Developing Countries: Is There a Link?," Public Administration & Development, Blackwell Publishing, vol. 35(5), pages 329-346, December.
    19. Baharumshah, Ahmad Zubaidi & Slesman, Ly & Wohar, Mark E., 2016. "Inflation, inflation uncertainty, and economic growth in emerging and developing countries: Panel data evidence," Economic Systems, Elsevier, vol. 40(4), pages 638-657.
    20. Balazs Egert, 2022. "Public policy reforms and their impact on productivity, investment and employment: new evidence from OECD and non-OECD countries," Public Sector Economics, Institute of Public Finance, vol. 46(2), pages 179-205.

    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:gam:jrisks:v:11:y:2023:i:12:p:207-:d:1289443. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.