Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
권호 표지
DOI QR코드

DOI QR Code

Sustainability of pensions in Asian countries

  • Hyunoo, Shim (Department of Actuarial Science, Hanyang University) ;
  • Siok, Kim (Department of Finance and Insurance, Hanyang University) ;
  • Yang Ho, Choi (Department of Actuarial Science, Hanyang University)
  • Received : 2022.05.05
  • Accepted : 2022.08.01
  • Published : 2022.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Mortality risk is a significant threat to individual life, and quantifying the risk is necessary for making a national population plan and is a traditionally fundamental task in the insurance and annuity businesses. Like other advanced countries, the sustainability of life pensions and the management of longevity risks are becoming important in Asian countries entering the era of aging society. In this study, mortality and pension value sustainability trends are compared and analyzed based on national population and mortality data, focusing on four Asian countries from 1990 to 2017. The result of analyzing the robustness and accuracy of generalized linear/nonlinear models reveals that the Cairns-Blake-Dowd model, the nonparametric Renshaw-Haberman model, and the Plat model show low stability. The Currie, CBD M5, M7, and M8 models have high stability against data periods. The M7 and M8 models demonstrate high accuracy. The longevity risk is found to be high in the order of Taiwan, Hong Kong, Korea, and Japan, which is in general inversely related to the population size.

Keywords

References

  1. Bartlett HP and Phillips DR (1995). Aging trends-Hong Kong, Journal of Cross-Cultural Gerontology, 10, 257-265. https://doi.org/10.1007/BF00972243
  2. Bell W and Monsell B (1991). Using principal components in time series modeling and forecasting of age-specific mortality rates. In Proceedings of the Social Statistics Section, 154-159.
  3. Bozikas A and Pitselis G (2018). An empirical study on stochastic mortality modelling under the age-period-cohort framework: The case of greece with applications to insurance pricing, Risks, 6, 44.
  4. Brouhns N, Denuit M, and Keilegom IV (2005). Bootstrapping the Poisson log-bilinear model for mortality forecasting, Scandinavian Actuarial Journal, 2005, 212-224. https://doi.org/10.1080/03461230510009754
  5. Buckham D, Wahl J, and Rose S (2010). Executive's Guide to Solvency II, John Wiley & Sons, New Jersey.
  6. Cairns AJG, Blake D, and Dowd K (2006). A two-factor model for stochastic mortality with parameter uncertainty: Theory and calibration, Journal of Risk And Insurance, 73, 687-718. https://doi.org/10.1111/j.1539-6975.2006.00195.x
  7. Cairns AJG, Blake D, Dowd K, Coughlan GD, Epstein D, Ong A, and Balevich I (2009). A quantitative comparison of stochastic mortality models using data from England and Wales and the United States, North American Actuarial Journal, 13, 1-35. https://doi.org/10.1080/10920277.2009.10597538
  8. Cairns AJG, Blake D, Dowd K, Coughlan GD, Epstein D, and Khalaf-Allah M (2011). Mortality density forecasts: An analysis of six stochastic mortality models, Insurance: Mathematics And Economics, 48, 355-367. https://doi.org/10.1016/j.insmatheco.2010.12.005
  9. Central Intelligence Agency (2022). The World Factbook. Available from: https://www.cia.gov
  10. Chung WJ (2007). Stochastic forecasting health expenditure with the application to the Korea's national health insurance system, Korean Social Security Studies, 23, 249-270.
  11. Coffie E (2015). A Comparison of Poisson or Negative Binomial Regression and Lee-Carter Models of Forecasting Norwegian Male Mortality, Master's Thesis, University of Oslo.
  12. Currie I (2006). Smoothing and Forecasting Mortality Rates with p-Splines, Presentation, London.
  13. Currie ID (2016). On fitting generalized linear and non-linear models of mortality, Scandinavian Actuarial Journal, 2016, 356-383.
  14. Feng L and Shi Y (2018). Forecasting mortality rates: Multivariate or univariate models?, Journal of Population Research, 35, 289-318. https://doi.org/10.1007/s12546-018-9205-z
  15. Giacometti R, Bertocchi M, Rachev ST, and Fabozzi FJ (2012). A comparison of the Lee-Carter model and AR-ARCH model for forecasting mortality rates, Insurance: Mathematics and Economics, 50, 85-93. https://doi.org/10.1016/j.insmatheco.2011.10.002
  16. Girosi F and King G (2008). Demographic Forecasting, Princeton University Press, New Jersey.
  17. Hunt A and Blake D (2015). Identifiability in age/period/cohort mortality models, Annals of Actuarial Science, 14, 500-536. https://doi.org/10.1017/S1748499520000123
  18. Hunt A and Villegas AM (2015). Robustness and convergence in the Lee-Carter model with cohort effects, Insurance: Mathematics And Economics, 64, 186-202. https://doi.org/10.1016/j.insmatheco.2015.05.004
  19. Hwang J-Y, Thao BT, and Ko B (2018). A Bayesian comparative study on mortality improvements across nations using the Lee-Carter model, The Journal of Risk Management, 29, 89-112. https://doi.org/10.21480/tjrm.29.1.201803.003
  20. Hyndman RJ, Koehler AB, Snyder RD, and Grose S (2002). A state space framework for automatic forecasting using exponential smoothing methods, International Journal of Forecasting, 18, 439-454. https://doi.org/10.1016/S0169-2070(01)00110-8
  21. Hyndman RJ and Ullah S (2007). Robust forecasting of mortality and fertility rates: A functional data approach, Computational Statistics & Data Analysis, 51, 4942-4956. https://doi.org/10.1016/j.csda.2006.07.028
  22. Jho JH (2020). Applications of stochastic interest and mortality in insurance risk calculation, Korean Insurance Journal, 122, 1-34.
  23. Kang H, Han ST, and Lee SK (2015). A Study on assessment index for long-term care insurance assessment committee using logistic regression model, Journal of the Korean Data Analysis Society, 17, 3015-3023.
  24. Kim TH (2006). Mortality forecasting for population projection, Korea Journal of Population Studies, 29, 27-51.
  25. Kim S (2012). A comparison study on the stochastic mortality models for measuring longevity risk, Korean Insurance Journal, 93, 213-236.
  26. Kim S (2013). A comparison study on methods of assessing longevity risk, Journal of Insurance and Finance, 24, 93-121.
  27. Koissi MC, Shapiro AF, and Hognas G (2006). Evaluating and extending the Lee-Carter model for mortality forecasting: Bootstrap confidence interval, Insurance: Mathematics and Economics, 38, 1-20. https://doi.org/10.1016/j.insmatheco.2005.06.008
  28. Le M, Xiao X, Pamucar D, and Liang Q (2021). A study on fiscal risk of China's employees basic pension system under longevity risk, Sustainability, 13, 5526.
  29. Le TTN and Kwon HS (2021). Suitability of stochastic models for mortality projection in Korea: A follow-up discussion, Communications for Statistical Applications and Methods, 28, 171-188. https://doi.org/10.29220/CSAM.2021.28.2.171
  30. Lee R and Miller T (2001). Evaluating the performance of the Lee-Carter method for forecasting mortality, Demography, 38, 537-549. https://doi.org/10.1353/dem.2001.0036
  31. Lee RD and Carter L (1992). Modeling and forecasting US mortality, Journal of the American Statistical Association, 87, 659-671. https://doi.org/10.2307/2290201
  32. Lovasz E (2011). Analysis of Finnish and Swedish mortality data with stochastic mortality models, European Actuarial Journal, 1, 259-289. https://doi.org/10.1007/s13385-011-0039-8
  33. Millossovich P, Villegas AM, and Kaishev VK (2018). StMoMo: An R package for stochastic mortality modelling, Journal of Statistical Software, 84, 1-38.
  34. Neves C, Fernandes C, and Hoeltgebaum H (2017). Five different distributions for the Lee-Carter model of mortality forecasting: A comparison using GAS models, Insurance: Mathematics and Economics, 75, 48-57. https://doi.org/10.1016/j.insmatheco.2017.04.004
  35. Plat R (2009). On stochastic mortality modeling, Insurance: Mathematics and Economics, 45, 393-404. https://doi.org/10.1016/j.insmatheco.2009.08.006
  36. Renshaw AE and Haberman S (2003). Lee-Carter mortality forecasting with age-specific enhancement, Insurance: Mathematics and Economics, 33, 255-272. https://doi.org/10.1016/S0167-6687(03)00138-0
  37. Renshaw AE and Haberman S (2006). A cohort-based extension to the Lee-Carter model for mortality reduction factors, Insurance: Mathematics and Economics, 38, 556-570. https://doi.org/10.1016/j.insmatheco.2005.12.001
  38. Richards SJ (2016). Mis-estimation risk: Measurement and impact, British Actuarial Journal, 21, 429-457. https://doi.org/10.1017/s1357321716000040
  39. Richards SJ, Currie ID, and Ritchie GP (2014). A value-at-risk framework for longevity trend risk, British Actuarial Journal, 19, 116-139. https://doi.org/10.1017/S1357321712000451
  40. Stefani A and Kwon HS (2021). A multi-state model approach for risk analysis of pensions for married couples with consideration of mortality difference by marital status, Communications for Statistical Applications and Methods, 28, 611-626. https://doi.org/10.29220/CSAM.2021.28.6.611
  41. Tian Y and Zhao X (2016). Stochastic forecast of the financial sustainability of basic pension in China, Sustainability, 8, 46.
  42. University of California, Berkeley (USA) and Max Planck Institute for Demographic Research (Germany) (2021). Human Mortality Database. Available from: https://www.mortality.org
  43. Villegas AM, Kaishev VK, and Millossovich P (2018). StMoMo: An R package for stochastic mortality modeling, Journal of Statistical Software, 84, 1-38.
  44. Won CH (2010). Estimation of longevity risk and funding ratio of public pension, The Korean Journal of Financial Management, 27, 1-26.