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Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates

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  • Nurul Aityqah Yaacob

    (Faculty of Science, Institute of Mathematical Sciences, University Malaya, Kuala Lumpur 50603, Malaysia
    Faculty of Computer and Mathematical Sciences, Universiti Teknologi Mara Cawangan Negeri Sembilan, Kuala Pilah 72000, Malaysia)

  • Jamil J. Jaber

    (Department of Finance, Faculty of Business, The University of Jordan/Aqaba branch, Aqaba 77110, Jordan)

  • Dharini Pathmanathan

    (Faculty of Science, Institute of Mathematical Sciences, University Malaya, Kuala Lumpur 50603, Malaysia)

  • Sadam Alwadi

    (Department of Finance, Faculty of Business, The University of Jordan/Aqaba branch, Aqaba 77110, Jordan)

  • Ibrahim Mohamed

    (Faculty of Science, Institute of Mathematical Sciences, University Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

This study implements various, maximum overlap, discrete wavelet transform filters to model and forecast the time-dependent mortality index of the Lee-Carter model. The choice of appropriate wavelet filters is essential in effectively capturing the dynamics in a period. This cannot be accomplished by using the ARIMA model alone. In this paper, the ARIMA model is enhanced with the integration of various maximal overlap discrete wavelet transform filters such as the least asymmetric, best-localized, and Coiflet filters. These models are then applied to the mortality data of Australia, England, France, Japan, and USA. The accuracy of the projecting log of death rates of the MODWT-ARIMA model with the aforementioned wavelet filters are assessed using mean absolute error, mean absolute percentage error, and mean absolute scaled error. The MODWT-ARIMA (5,1,0) model with the BL14 filter gives the best fit to the log of death rates data for males, females, and total population, for all five countries studied. Implementing the MODWT leads towards improvement in the performance of the standard framework of the LC model in forecasting mortality rates.

Suggested Citation

  • Nurul Aityqah Yaacob & Jamil J. Jaber & Dharini Pathmanathan & Sadam Alwadi & Ibrahim Mohamed, 2021. "Hybrid of the Lee-Carter Model with Maximum Overlap Discrete Wavelet Transform Filters in Forecasting Mortality Rates," Mathematics, MDPI, vol. 9(18), pages 1-11, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:18:p:2295-:d:637535
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    References listed on IDEAS

    as
    1. Hainaut, Donatien & Denuit, Michel, 2020. "Wavelet-Based Feature Extraction For Mortality Projection," ASTIN Bulletin, Cambridge University Press, vol. 50(3), pages 675-707, September.
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    3. Abdullah H. Alenezy & Mohd Tahir Ismail & S. Al Wadi & Muhammad Tahir & Nawaf N. Hamadneh & Jamil J. Jaber & Waqar A. Khan & Basil K. Papadopoulos, 2021. "Forecasting Stock Market Volatility Using Hybrid of Adaptive Network of Fuzzy Inference System and Wavelet Functions," Journal of Mathematics, Hindawi, vol. 2021, pages 1-10, August.
    4. Neves, César & Fernandes, Cristiano & Hoeltgebaum, Henrique, 2017. "Five different distributions for the Lee–Carter model of mortality forecasting: A comparison using GAS models," Insurance: Mathematics and Economics, Elsevier, vol. 75(C), pages 48-57.
    5. Hyndman, Rob J. & Shahid Ullah, Md., 2007. "Robust forecasting of mortality and fertility rates: A functional data approach," Computational Statistics & Data Analysis, Elsevier, vol. 51(10), pages 4942-4956, June.
    6. Hainaut, Donatien & Denuit, Michel, 2020. "Wavelet-based feature extraction for mortality projection," LIDAM Reprints ISBA 2020008, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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    Cited by:

    1. Abdullah H. Alenezy & Mohd Tahir Ismail & Sadam Al Wadi & Jamil J. Jaber, 2023. "Predicting Stock Market Volatility Using MODWT with HyFIS and FS.HGD Models," Risks, MDPI, vol. 11(7), pages 1-16, July.

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    Keywords

    MODWT; DWT; BL14; Coiflet; least asymmetric; wavelet;
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