Multi-population mortality models: fitting, forecasting and comparisons

Enchev, Vasil; Kleinow, Torsten; Cairns, Andrew J. G.

doi:10.1080/03461238.2015.1133450

Cited by 76 publications

(55 citation statements)

References 14 publications

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“…Mortality forecasts are, currently, almost exclusively performed using models which decompose age-specific mortality rates into age, period, and sometimes also cohort effects, inspired by the Lee and Carter (1992) (LC) model, which is the most popular mortality forecasting model in countries with data of high quality (Booth, 2006;Cairns et al, 2009;Enchev et al, 2017). One major limitation with the LC type of models is that they generally underestimate improvements in mortality as a result of assuming constant age-specific and relative improvements (Bergeron-Boucher et al, 2017a;Booth and Tickle, 2008).…”

Section: Introductionmentioning

confidence: 99%

Longevity Forecasting by Socio-Economic Groups Using Compositional Data Analysis

Kjærgaard

Ergemen

Bergeron-Boucher

et al. 2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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Several OECD countries have recently implemented an automatic link between the statutory retirement age and life expectancy for the total population to insure sustainability in their pension systems when life expectancy is increasing. Significant mortality differentials are observed across socio-economic groups and future changes in these differentials will determine whether some socio-economic groups drive increases in the retirement age leaving other groups with fewer years in receipt of pensions. We forecast life expectancy by socio-economic groups and compare the forecast performance of competing models using Danish mortality data and find that the most accurate model assumes a common mortality trend. Life expectancy forecasts are used to analyse the consequences of a pension system where the statutory retirement age is increased when total life expectancy is increasing.

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Section: Introductionmentioning

confidence: 99%

Longevity Forecasting by Socio-Economic Groups Using Compositional Data Analysis

Kjærgaard

Ergemen

Bergeron-Boucher

et al. 2020

Journal of the Royal Statistical Society Series A: Statistics in Society

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“…75 See also Jarner and Kryger 2011 Still living with mortality multi-population models. Despite the general popularity of the Li & Lee (2005) model, their model has been found to be quite unsuitable for some actuarial applications by both Villegas et al (2017) and Enchev et al (2017). Specifically, applications that require a stochastic assessment of longevity risk (e.g.…”

Section: Extrapolative or Time Series Models -Multi-population Variantsmentioning

confidence: 99%

Still Living With Mortality: The Longevity Risk Transfer Market After One Decade

et al. 2018

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This paper updates Living with Mortality published in 2006. It describes how the longevity risk transfer market has developed over the intervening period, and, in particular, how insurance-based solutionsbuy-outs, buy-ins and longevity insurancehave triumphed over capital markets solutions that were expected to dominate at the time. Some capital markets solutionslongevity-spread bonds, longevity swaps, q-forwards and tail-risk protectionhave come to market, but the volume of business has been disappointingly low. The reason for this is that when market participants compare the index-based solutions of the capital markets with the customised solutions of insurance companies in terms of basis risk, credit risk, regulatory capital, collateral and liquidity, the former perform on balance less favourably despite a lower potential cost. We discuss the importance of stochastic mortality models for forecasting future longevity and examine some applications of these models, e.g. determining the longevity risk premium and estimating regulatory capital relief. The longevity risk transfer market is now beginning to recognise that there is insufficient capacity in the insurance and reinsurance industries to deal fully with demand and new solutions for attracting capital markets investors are now being examinedsuch as longevity-linked securities and reinsurance sidecars.

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“…In this section we provide an overview of different approaches to modelling mortality as well as their estimation methodologies. Our focus will be on single population mortality modelling, however the essential elements of the approaches and methods discussed can be carried over to multi-population settings, see for example Enchev et al (2016).…”

Section: State-space Approach To Mortality Modellingmentioning

confidence: 99%

“…The second approach employs regression-based methods to calibrate mortality models (Brouhns et al, 2002). For some recent studies based on this approach, see O'Hare and Li (2012), van Berkum et al (2016 and Enchev et al (2016). The recent paper Currie (2016) provides a comprehensive summary on mortality modelling based on the generalised linear modelling (GLM) framework.…”

Section: Introductionmentioning

confidence: 99%

Cohort effects in mortality modelling: a Bayesian state-space approach

Fung

Peters²,

Shevchenko³

2018

Ann. actuar. sci.

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Cohort effects are important factors in determining the evolution of human mortality for certain countries. Extensions of dynamic mortality models with cohort features have been proposed in the literature to account for these factors under the generalised linear modelling framework. In this paper we approach the problem of mortality modelling with cohort factors incorporated through a novel formulation under a state-space methodology. In the process we demonstrate that cohort factors can be formulated naturally under the state-space framework, despite the fact that cohort factors are indexed according to year-of-birth rather than year. Bayesian inference for cohort models in a state-space formulation is then developed based on an efficient Markov chain Monte Carlo sampler, allowing for the quantification of parameter uncertainty in cohort models and resulting mortality forecasts that are used for life expectancy and life table constructions. The effectiveness of our approach is examined through comprehensive empirical studies involving male and female populations from various countries. Our results show that cohort patterns are present for certain countries that we studied and the inclusion of cohort factors are crucial in capturing these phenomena, thus highlighting the benefits of introducing cohort models in the state-space framework. Forecasting of cohort models is also discussed in light of the projection of cohort factors.

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Multi-population mortality models: fitting, forecasting and comparisons

Cited by 76 publications

References 14 publications

Longevity Forecasting by Socio-Economic Groups Using Compositional Data Analysis

Longevity Forecasting by Socio-Economic Groups Using Compositional Data Analysis

Still Living With Mortality: The Longevity Risk Transfer Market After One Decade

Cohort effects in mortality modelling: a Bayesian state-space approach

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