Work

Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections

Year: 2024

Type: article

Abstract: Forecasting mortality rates is crucial for evaluating life insurance company solvency, especially amid disruptions caused by phenomena like COVID-19. The Lee-Carter model is commonly employed in mortality modelling; however, extensions that can encompass count data with diverse distributions, such as the Generalized Autoregressive Score (GAS) model utilizing the COM-Poisson distribution, exhibit potential for enhancing time-to-event forecasting accuracy. Using mortality data from 29 countries, this research evaluates various distributions and determines that the COM-Poisson model surpasses the Poisson, binomial, and negative binomial distributions in forecasting mortality rates. The one-step forecasting capability of the GAS model offers distinct advantages, while the COM-Poisson distribution demonstrates enhanced flexibility and versatility by accommodating various distributions, including Poisson and negative binomial. Ultimately, the study determines that the COM-Poisson GAS model is an effective instrument for examining time series data on mortality rates, particularly when facing time-varying parameters and non-conventional data distributions.

Source: Lifetime Data Analysis

DOI: 10.1007/s10985-024-09634-x

Authors: Suryo Adi Rakhmawan, Tahir Mahmood, Nasir Abbas, Muhammad Riaz

Institutions: Badan Pusat Statistik, University of the West of Scotland, King Fahd University of Petroleum and Minerals

Language: English

Topic: Insurance, Mortality, Demography, Risk Management

Subfield: Demography

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,990

OpenAlex ID: W4402517012