Mortality models and longevity risk for small populations

Wang, Hsin-Chung; Yue, Ching-Syang Jack; Chong, Chen-Tai

doi:10.1016/j.insmatheco.2017.09.020

Cited by 13 publications

(4 citation statements)

References 12 publications

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“…To this end, our paper also taps the literature of multipopulation mortality modelling. Started by Li and Lee (2005), a number of two-and multi-population mortality models have been proposed to enhance mortality modelling and reduce basis risk (see, e.g., Dowd et al, 2011;Jarner and Kryger, 2011;Zhou et al, 2013;Chen et al, 2015;Wang et al, 2015Wang et al, , 2018Li et al, 2017;Zhu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Neighbouring Prediction for Mortality

Wang¹,

Zhang²,

Zhu³

2021

ASTIN Bull.

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We propose a new neighbouring prediction model for mortality forecasting. For each mortality rate at age x in year t, mx,t, we construct an image of neighbourhood mortality data around mx,t, that is, Ꜫ mx,t (x1, x2, s), which includes mortality information for ages in [x-x1, x+x2], lagging k years (1 ≤ k ≤ s). Combined with the deep learning model – convolutional neural network, this framework is able to capture the intricate nonlinear structure in the mortality data: the neighbourhood effect, which can go beyond the directions of period, age, and cohort as in classic mortality models. By performing an extensive empirical analysis on all the 41 countries and regions in the Human Mortality Database, we find that the proposed models achieve superior forecasting performance. This framework can be further enhanced to capture the patterns and interactions between multiple populations.

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Section: Introductionmentioning

confidence: 99%

Neighbouring Prediction for Mortality

Wang¹,

Zhang²,

Zhu³

2021

ASTIN Bull.

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“…To this end, our paper also taps the literature of multi-population mortality modeling. Started by Li and Lee (2005), a number of two-and multi-population mortality models have been proposed to enhance mortality modeling and reduce basis risk (see, e.g., Dowd et al, 2011;Jarner and Kryger, 2011;Zhou et al, 2013;Chen et al, 2015;Wang et al, 2015;Li et al, 2017;Zhu et al, 2017;Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Neighbouring Prediction for Mortality

2020

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“…• Kleinov (2015), who developed a common age affect variants of the Lee-Carter model with p age and period factors for modeling the mortality of multiple populations; • Li et al (2015), who generalize a single-population mortality model in different possible ways, in order to fit two or more populations and to measure the basis risk in longevity hedges; • Wan and Bertschi (2015), who proposed a two part model to fit Swiss historical data and make coherent forecasts, taking information from a larger population; • Antonio et al (2017), who developed a Li and Lee multi-population model to project Dutch and Belgian mortality evolution and measure the actuarial implication of their model; • Chen et al (2017), who proposed "the use of parametric bootstrap methods to investigate the finite sample distribution of the maximum likelihood estimator for the parameter vector of a stochastic mortality model"; • Hunt and Blake (2017), who modeled the mortality rates of a pension scheme through an Age Period Cohort (APC) model that has the same form of the reference population model but is characterized by scaling factors that multiply period and cohort parameters and reduce or increase the dependence between the two models; • Villegas et al (2017), who developed a comprehensive comparative study of mortality models for two populations proposed in the literature and applied them to the case of a population of a pension scheme in order to measure the basis risk involved in longevity hedges; • Wang et al (2018), who proposed an approach based on a combination of data aggregation and mortality graduation applied to the empirical data from Taiwan and Taipei City.…”

Section: Introductionmentioning

confidence: 99%

Mortality Projections for Small Populations: An Application to the Maltese Elderly

Menzietti

Morabito

Stranges

2019

Risks

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In small populations, mortality rates are characterized by a great volatility, the datasets are often available for a few years and suffer from missing data. Therefore, standard mortality models may produce high uncertain and biologically improbable projections. In this paper, we deal with the mortality projections of the Maltese population, a small country with less than 500,000 inhabitants, whose data on exposures and observed deaths suffers from all the typical problems of small populations. We concentrate our analysis on older adult mortality. Starting from some recent suggestions in the literature, we assume that the mortality of a small population can be modeled starting from the mortality of a bigger one (the reference population) adding a spread. The first part of the paper is dedicated to the choice of the reference population, then we test alternative mortality models. Finally, we verify the capacity of the proposed approach to reduce the volatility of the mortality projections. The results obtained show that the model is able to significantly reduce the uncertainty of projected mortality rates and to ensure their coherent and biologically reasonable evolution.

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Mortality models and longevity risk for small populations

Cited by 13 publications

References 12 publications

Neighbouring Prediction for Mortality

Neighbouring Prediction for Mortality

Neighbouring Prediction for Mortality

Mortality Projections for Small Populations: An Application to the Maltese Elderly

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