Modelling and Forecasting the Mortality Risks to Investigate its Impact on Insurance Companies in Malaysia: Poisson Lee-Carter Model and CairnsBlake-Dowd Model

Authors

  • Ong Ying Ming School of Actuarial Science, Mathematics, and Qualitative Study (SOMAQS), Asia Pacific University of Technology and Innovation, Malaysia Author
  • Raja Rajeswari School of Actuarial Science, Mathematics, and Qualitative Study (SOMAQS), Asia Pacific University of Technology and Innovation, Malaysia Author
  • Hazlina Darman School of Actuarial Science, Mathematics, and Qualitative Study (SOMAQS), Asia Pacific University of Technology and Innovation, Malaysia Author

DOI:

https://doi.org/10.61841/p8mk5809

Keywords:

Forecasting, Mortality rate, Lee-Carter model, Poisson Lee-Carter model, Cairns-Blake-Dowd model

Abstract

The objective of this paper is to model and forecast the future mortality rates based on the data of insurance companies in Malaysia. This paper compares and applies three stochastic mortality models which are Lee-Carter, Poisson Lee-Carter and Cairns-Blake-Dowd models to forecast mortality rates for population of Malaysia. All genders and all single age data are fitted to all the models from year 2000 to year 2015. Based on the results, it shows that the fitted and forecasted values are having the different pattern of time index for Lee-Carter of family model and Cairns-Blake-Dowd model. To estimate the appropriate model among these models used in this study, the maximum log likelihood, AIC and BIC are used. According to the results of goodness of fit, Poisson LeeCarter model has the best and the lowest values compared to other two models. The results from MAE and MAPE indicate that the forecast values are closer to the actual value. Therefore, Poisson Lee-Carter model is a good fit model to forecast Malaysia mortality rate. 

Downloads

Download data is not yet available.

References

1. Biehl, J., 2013. The Impact of life: How Mortality Improvement Affects the Life Insurance Industry Today. Product Matters!, Issue 85, pp. 23-25.

2. Blake, D., Andrew Cairns, K. D. & Kessler, A., 2018. Still Living With Mortality: The Longevity Risk Transfer Market After One Decade, Edinburgh: The Institute and Faculty of Actuaries.

3. Bozikas, A. & Pitselis, G., 2018. An Empirical Study on Stochastic Mortality Modelling under the Age-PeriodCohort Framework: The Case of Greece with Applications to Insurance Pricing. Risks, 6(44), p. 34.

4. Brouhns, N., Denuit, M. & Vermunt, J. K., 2002. A Poisson log-bilinear regression approach to the construction of projected lifetables. Mathematics and Economics , Volume 31, pp. 373-393.

5. Brouhns, N., Van Keilegom, I. & Denuit, M., 2005. Boostrapping the Poisson Log-Bilinear Model for Mortality Forecasting. Scandanavian Actuarial Journal, Volume 3, pp. 212-224.

6. Cairns, A. J. G., Blake, D. & Dowd, K., 2006. A TWO-FACTOR MODEL FOR STOCHASTIC MORTALITY WITH PARAMETER UNCERTAINTY: THEORY AND CALIBRATION. The Journal of Risk and Insurance,, 73(4), pp. 687-718.

7. Crawford, T., Haan, R. d. & Runchey, C., 2008. Longevity Risk Quantification and Management: A Review of Relevant Literature, United State: Society of Actuaries.

8. Gatzert, N. & Wesker, H., 2014. Mortality Risk and its Effect on Shortfall and Risk Management in Life Insurance. The Journal of Risk and Insurance, 81(1), pp. 57-90.

9. Gogola, J., 2014. A Comparison of Lee-Carter and Cairns-Blake-Dowd Stochastic Mortality Model.

Mathematical Methods in Science and Mechanics, pp. 130-134.

10. Hu, Y. & Cox, S. H., 2005. Modelling Mortality Risk from Exposure to A Potential Future Exreme Event and

its Impact on Life Insurance. Department of Risk Management and Insurance.

11. Ibrahim, R. I. & Siri, Z., 2015. A Study on Longevity Factor: The Case of Government Pensioner in Malaysia.

International Journal of BUsiness and Society, 16(1), pp. 147-161.

12. Koissi, M.-C., Shapiro, A. F. & Högnäs, G., 2006. Evaluating and extending the Lee–Carter model for mortality

forecasting: Bootstrap confidence interval. Insurance: Mathematics and Economics, 38(1), pp. 1-20.

13. Lee, R. & Nault, F., 1993. Modeling and forecasting provincial mortality in Canada, Montre’al: World

Congress of International.

14. Lee, R. & Rofman, R., 1994. Modelacion y proyeccion de la mortalidad in Chile (Modeling and projecting

mortality in Chile). Natas, 22(59), pp. 182-313.

15. Li, J. et al., 2012. Mortality Experience in Asia-Pacific and Modelling and Management of Longevity Risk.

Insurance Risk and Finance Research Centre .

16. Maccheroni, C. & Nocito, S., 2017. Backtesting the Lee-Carter and the Cairns-Blake-Dowd Stochastic

Mortality Models on Italian Death Rates. Risk 2017, Issue 34, p. 5.

17. Melnikov, A. & Romaniuk, Y., 2006. Evaluating the performance of Gompertz, Makeham and Lee–Carter

mortality models for risk management with unit-linked contracts. Insurance: Mathematics and Economics,

39(3), pp. 310-329.

18. Ngataman, N., Ibrahim, R. I. & Yusuf, M. M., 2016. Forecasting the mortality rates of Malaysian population

using Lee-Carter method, Malaysia: American Institute of Physics.

19. Stoeldraijer, L., Duin, C. V., Wissen, L. V. & Janssen, F., 2013. Impact of Different Mortality Forecasting

Methods and Explicit Assumptions on Projected Future Life Expectancy: The Case of the Netherlands. Journal

of Population Sciences., 29(13), pp. 323-354.

20. Villegas, A. M., Millossovich, P. & Kaishev, V. K., 2018. StMoMo: An R Package for Stochastic Mortality. R

Package.

21. Wilmoth, J., 1996. Mortality projections for Japan: a comparison of four methods, New York: Oxford

University Press.

Downloads

Published

30.04.2020

How to Cite

Ying Ming, O., Rajeswari, R., & Darman, H. (2020). Modelling and Forecasting the Mortality Risks to Investigate its Impact on Insurance Companies in Malaysia: Poisson Lee-Carter Model and CairnsBlake-Dowd Model. International Journal of Psychosocial Rehabilitation, 24(2), 857-872. https://doi.org/10.61841/p8mk5809