Abstract
The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies’ profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee–Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.
Highlights
The accuracy of the prediction of age-specific probabilities of death is the main objective for life insurance companies
This paper has described the usefulness and simplicity of resampling methods and a radial plotting technique called radar plotting for multivariate graphical data
They are commonly used in other fields such as business management and engineering, these methods have not found a foothold in the presentation of research results related to mortality forecasting models
Summary
The accuracy of the prediction of age-specific probabilities of death is the main objective for life insurance companies. Sophisticated models have been implemented in the actuarial literature to improve the accuracy of the future age-specific probabilities of death. Among the stochastic mortality models, the Lee–Carter model [1] is one of the most well-known and applied methods in the demographic and actuarial fields. This model [1] has inspired numerous variants and extensions to improve the goodness-of-fit and the forecasting properties of the model since its publication in 1992 [2,3,4]. In recent years, different models have been developed to calibrate mortality with different methodologies [9,10,11,12]
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