Modeling Mortality with Kernel Principal Component Analysis (KPCA) Method

Journal article

Yuanqi Wu; Andrew Chen; Yanbin Xu; Guangming Pan; Wenjun Zhu

Annals of Actuarial Science · 2024-11 · Volume 18 · Issue 3 · pp. 626-643
DOI: 10.1017/s1748499524000277
Published online: 2024-12-03 · Download citation (BibTeX)

Publisher

Abstract

As the global population continues to age, effective management of longevity risk becomes increasingly critical for various stakeholders. Accurate mortality forecasting serves as a cornerstone for addressing this challenge. This study proposes to leverage Kernel Principal Component Analysis (KPCA) to enhance mortality rate predictions. By extending the traditional Lee-Carter model with KPCA, we capture nonlinear patterns and complex relationships in mortality data. The newly proposed KPCA Lee-Carter algorithm is empirically tested and demonstrates superior forecasting performance. Furthermore, the model’s robustness was tested during the COVID-19 pandemic, showing that the KPCA Lee-Carter algorithm effectively captures increased uncertainty during extreme events while maintaining narrower prediction intervals. This makes it a valuable tool for mortality forecasting and risk management. Our findings contribute to the growing body of literature where actuarial science intersects with statistical learning, offering practical solutions to the challenges posed by an aging world population.

Keywords: Mortality forecasting; Machine learning; Kernel Principal Component Analysis; Longevity risk; Predictive analytics

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