IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v10y2022i9p183-d915479.html
   My bibliography  Save this article

Modelling USA Age-Cohort Mortality: A Comparison of Multi-Factor Affine Mortality Models

Author

Listed:
  • Zhiping Huang

    (School of Risk and Actuarial Studies, Australian Research Council Centre of Excellence in Population Ageing Research (CEPAR), UNSW Sydney, Sydney, NSW 2052, Australia)

  • Michael Sherris

    (School of Risk and Actuarial Studies, Australian Research Council Centre of Excellence in Population Ageing Research (CEPAR), UNSW Sydney, Sydney, NSW 2052, Australia)

  • Andrés M. Villegas

    (School of Risk and Actuarial Studies, Australian Research Council Centre of Excellence in Population Ageing Research (CEPAR), UNSW Sydney, Sydney, NSW 2052, Australia)

  • Jonathan Ziveyi

    (School of Risk and Actuarial Studies, Australian Research Council Centre of Excellence in Population Ageing Research (CEPAR), UNSW Sydney, Sydney, NSW 2052, Australia)

Abstract

Affine mortality models are well suited for theoretical and practical application in pricing and risk management of mortality risk. They produce consistent, closed-form stochastic survival curves allowing for the efficient valuation of mortality-linked claims. We model USA age-cohort mortality data using five multi-factor affine mortality models. We focus on three-factor models and compare four Gaussian models along with a model based on the Cox–Ingersoll–Ross (CIR) process, allowing for Gamma-distributed mortality rates. We compare and assess the Gaussian Arbitrage-Free Nelson–Siegel (AFNS) mortality model, which incorporates level, slope and curvature factors, and the canonical Gaussian factor model, both with and without correlations in the factor dynamics. We show that for USA mortality data, the probability of negative mortality rates in the Gaussian models is small. Models are estimated using discrete time versions of the models with age-cohort data capturing variability in cohort mortality curves. Poisson variation in mortality data is included in the model estimation using the Kalman filter through the measurement equation. We consider models incorporating factor dependence to capture the effects of age-dependence in the mortality curves. The analysis demonstrates that the Gaussian independent-factor AFNS model performs well compared to the other affine models in explaining and forecasting USA age-cohort mortality data.

Suggested Citation

  • Zhiping Huang & Michael Sherris & Andrés M. Villegas & Jonathan Ziveyi, 2022. "Modelling USA Age-Cohort Mortality: A Comparison of Multi-Factor Affine Mortality Models," Risks, MDPI, vol. 10(9), pages 1-28, September.
    • Handle: RePEc:gam:jrisks:v:10:y:2022:i:9:p:183-:d:915479
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/10/9/183/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/10/9/183/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Willets, R. C., 2004. "The Cohort Effect: Insights and Explanations," British Actuarial Journal, Cambridge University Press, vol. 10(4), pages 833-877, October.
    2. Yajing Xu & Michael Sherris & Jonathan Ziveyi, 2020. "Market Price of Longevity Risk for a Multi‐Cohort Mortality Model With Application to Longevity Bond Option Pricing," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 87(3), pages 571-595, September.
    3. Luciano, Elisa & Spreeuw, Jaap & Vigna, Elena, 2008. "Modelling stochastic mortality for dependent lives," Insurance: Mathematics and Economics, Elsevier, vol. 43(2), pages 234-244, October.
    4. Gregory R. Duffee, 2002. "Term Premia and Interest Rate Forecasts in Affine Models," Journal of Finance, American Finance Association, vol. 57(1), pages 405-443, February.
    5. Schrager, David F., 2006. "Affine stochastic mortality," Insurance: Mathematics and Economics, Elsevier, vol. 38(1), pages 81-97, February.
    6. Duan, Jin-Chuan & Simonato, Jean-Guy, 1999. "Estimating and Testing Exponential-Affine Term Structure Models by Kalman Filter," Review of Quantitative Finance and Accounting, Springer, vol. 13(2), pages 111-135, September.
    7. Jevtić, Petar & Luciano, Elisa & Vigna, Elena, 2013. "Mortality surface by means of continuous time cohort models," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 122-133.
    8. 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.
    9. 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.
    10. 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.
    11. Alois L. J. Geyer & Stefan Pichler, 1999. "A State‐Space Approach To Estimate And Test Multifactor Cox‐Ingersoll‐Ross Models Of The Term Structure," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(1), pages 107-130, March.
    12. Daniel H. Alai & Katja Ignatieva & Michael Sherris, 2019. "The Investigation of a Forward-Rate Mortality Framework," Risks, MDPI, vol. 7(2), pages 1-22, June.
    13. SriDaran, Dilan & Sherris, Michael & Villegas, Andrés M. & Ziveyi, Jonathan, 2022. "A Group Regularisation Approach For Constructing Generalised Age-Period-Cohort Mortality Projection Models," ASTIN Bulletin, Cambridge University Press, vol. 52(1), pages 247-289, January.
    14. De Rossi, Giuliano, 2004. "Kalman filtering of consistent forward rate curves: a tool to estimate and model dynamically the term structure," Journal of Empirical Finance, Elsevier, vol. 11(2), pages 277-308, March.
    15. Geyer, Alois L J & Pichler, Stefan, 1999. "A State-Space Approach to Estimate and Test Multifactor Cox-Ingersoll-Ross Models of the Term Structure," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(1), pages 107-130, Spring.
    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. Miguel Santolino, 2023. "Should Selection of the Optimum Stochastic Mortality Model Be Based on the Original or the Logarithmic Scale of the Mortality Rate?," Risks, MDPI, vol. 11(10), pages 1-21, September.

    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. Jennifer L. Wang & H.C. Huang & Sharon S. Yang & Jeffrey T. Tsai, 2010. "An Optimal Product Mix for Hedging Longevity Risk in Life Insurance Companies: The Immunization Theory Approach," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 77(2), pages 473-497, June.
    2. Høg, Espen P. & Frederiksen, Per H., 2006. "The Fractional Ornstein-Uhlenbeck Process: Term Structure Theory and Application," Finance Research Group Working Papers F-2006-01, University of Aarhus, Aarhus School of Business, Department of Business Studies.
    3. Jevtić, Petar & Regis, Luca, 2019. "A continuous-time stochastic model for the mortality surface of multiple populations," Insurance: Mathematics and Economics, Elsevier, vol. 88(C), pages 181-195.
    4. Rihab Bedoui & Islem Kedidi, 2018. "Modeling Longevity Risk using Consistent Dynamics Affine Mortality Models," Working Papers hal-01678050, HAL.
    5. Fadoua Zeddouk & Pierre Devolder, 2020. "Longevity Modelling and Pricing under a Dependent Multi-Cohort Framework," Risks, MDPI, vol. 8(4), pages 1-23, November.
    6. Blackburn, Craig & Sherris, Michael, 2013. "Consistent dynamic affine mortality models for longevity risk applications," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 64-73.
    7. Ngai, Andrew & Sherris, Michael, 2011. "Longevity risk management for life and variable annuities: The effectiveness of static hedging using longevity bonds and derivatives," Insurance: Mathematics and Economics, Elsevier, vol. 49(1), pages 100-114, July.
    8. Zhou, Hongjuan & Zhou, Kenneth Q. & Li, Xianping, 2022. "Stochastic mortality dynamics driven by mixed fractional Brownian motion," Insurance: Mathematics and Economics, Elsevier, vol. 106(C), pages 218-238.
    9. Kris Jacobs & Xiaofei Li, 2008. "Modeling the Dynamics of Credit Spreads with Stochastic Volatility," Management Science, INFORMS, vol. 54(6), pages 1176-1188, June.
    10. Esben Hoeg & Per Frederiksen, 2006. "The Fractional OU Process: Term Structure Theory and Application," Computing in Economics and Finance 2006 194, Society for Computational Economics.
    11. Jevtić, P. & Hurd, T.R., 2017. "The joint mortality of couples in continuous time," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 90-97.
    12. Petar Jevtić & Luca Regis, 2021. "A Square-Root Factor-Based Multi-Population Extension of the Mortality Laws," Mathematics, MDPI, vol. 9(19), pages 1-17, September.
    13. Virginia R. Young, 2007. "Pricing Life Insurance under Stochastic Mortality via the Instantaneous Sharpe Ratio: Theorems and Proofs," Papers 0705.1297, arXiv.org.
    14. Anastasia Novokreshchenova, 2016. "Predicting Human Mortality: Quantitative Evaluation of Four Stochastic Models," Risks, MDPI, vol. 4(4), pages 1-28, December.
    15. Jury Falini, 2009. "Pricing caps with HJM models: the benefits of humped volatility," Department of Economics University of Siena 563, Department of Economics, University of Siena.
    16. Caggiano, Giovanni & Leonida, Leone, 2007. "A note on the empirics of the neoclassical growth model," Economics Letters, Elsevier, vol. 94(2), pages 170-176, February.
    17. Maclachlan, Iain C, 2007. "An empirical study of corporate bond pricing with unobserved capital structure dynamics," MPRA Paper 28416, University Library of Munich, Germany.
    18. Elisa Luciano & Jaap Spreeuw & Elena Vigna, 2016. "Spouses’ Dependence across Generations and Pricing Impact on Reversionary Annuities," Risks, MDPI, vol. 4(2), pages 1-18, May.
    19. 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.
    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.

    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:10:y:2022:i:9:p:183-:d:915479. 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.