Title: Multivariate long memory cohort mortality models
Authors: Jennifer Chan - University of Sydney (Australia) [presenting]
Abstract: The existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee-Carter (LC) model to death counts and incorporating long memory model structure. Furthermore, there is no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long memory mortality model with long memory cohort effect (LMLM) model outperform multivariate extended LC cohort (MELCC) model in both in-sample fitting and outsample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.