A Bayesian Approach to Developing a Stochastic Mortality Model for China

Li, Johnny Siu Hang; Zhou, Kenneth Q.; Zhu, Xiaobai; Chan, William; Chan, Felix Hon Wai

doi:10.1111/rssa.12473

Cited by 15 publications

(9 citation statements)

References 56 publications

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“…First, the MI rates projected by these factor-based models are likely to be jagged across ages and/or cohorts, due to the non-smooth factor estimates. To overcome this problem, studies, such as Renshaw and Haberman [ 39 ], Delwarde et al [ 10 ], Currie [ 7 ], Pitt et al [ 36 ] and Li et al [ 33 ], have considered using penalization methods or splines to smooth the age-specific factors. Nevertheless, such smoothing methods can hardly be applied simultaneously to both age- and period-specific factors to achieve a smooth two-dimensional MI surface.…”

Section: Introductionmentioning

confidence: 99%

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Zhu

Zhou

2022

Eur. Actuar. J.

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This paper proposes a spline mortality model for generating smooth projections of mortality improvement rates. In particular, we follow the two-dimensional cubic B-spline approach developed by Currie et al. (Stat Model 4(4):279–298, 2004), and adopt the Bayesian estimation and LASSO penalty to overcome the limitations of spline models in forecasting mortality rates. The resulting Bayesian spline model not only provides measures of stochastic and parameter uncertainties, but also allows external opinions on future mortality to be consistently incorporated. The mortality improvement rates projected by the proposed model are smoothly transitioned from the historical values with short-term trends shown in recent observations to the long-term terminal rates suggested by external opinions. Our technical work is complemented by numerical illustrations that use real mortality data and external rates to showcase the features of the proposed model.

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Section: Introductionmentioning

confidence: 99%

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Zhu

Zhou

2022

Eur. Actuar. J.

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“…Especially, for countries with poor mortality data quality, the problem is more remarkable. In view of the large fluctuation of population mortality data in China sampling survey years and the lack of mortality data in some years, Siu et al [13] use the Gaussian condition space model to fill and correct the data under the Bayesian framework. However, this method is only for a single population and does not consider the relationship between populations [13].…”

Section: Introductionmentioning

confidence: 99%

Multiple Population Mortality Jointly Forecasting in China Using PCF Model

Ming

2022

Mathematical Problems in Engineering

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Based on the data of population mortality in China since 1994, this paper studies the changing trend of sex differences of mortality by proposing and implementing some novel approaches. First of all, this paper proposes a new method using the Poisson common factor (PCF) model to forecast the mortality jointly by both sexes. The study finds that the PCF model effectively captures the common trend of mortality between two sexes and uses additional factors to reflect differences of between two sexes, which can reduce the errors caused by low quality or large fluctuation of the mortality in China. Meanwhile, the forecasting values of mortality based on the PCF model can abide by the human biological law well, and the sex ratio of mortality converges to a fixed constant in the long run without increasing too much statistical error. Second, this paper improves the parameter estimation method of PCF model, and the innovative two-step method is used to estimate the model, which can make the maximum likelihood estimator converge more easily. Finally, as an application of the novel methods which are proposing in this study, we measure the longevity risk of pension by using the PCF model and find that the PCF model can make up the underestimation of longevity risk from traditional models and provide more scientific information to the sponsor of pension plan.

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“…Despite these benefits, the use of Bayesian methods in mortality modelling is still sparse and has been largely confined to DFM models (Czado et al, 2005;Pedroza, 2006;Reichmuth and Sarferaz, 2008;Cairns et al, 2011;Antonio et al, 2015;Li et al, 2015;van Berkum et al, 2017;Alexopoulos et al, 2019;Li et al, 2019;Wang et al, 2021). 3 To our knowledge, the only paper employing BVAR to mortality data is Njenga and Sherris (2020).…”

Section: Introductionmentioning

confidence: 99%

Modelling mortality: A bayesian factor-augmented var (favar) approach

Zhu²

2022

ASTIN Bull.

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Longevity risk is putting more and more financial pressure on governments and pension plans worldwide due to pensioners’ increasing trend of life expectancy and the growing numbers of people reaching retirement age. Lee and Carter (1992, Journal of the American Statistical Association, 87(419), 659–671.) applied a one-factor dynamic factor model to forecast the trend of mortality improvement, and the model has since become the field’s workhorse. It is, however, well known that their model is subject to the limitation of overlooking cross-dependence between different age groups. We introduce Factor-Augmented Vector Autoregressive (FAVAR) models to the mortality modelling literature. The model, obtained by adding an unobserved factor process to a Vector Autoregressive (VAR) process, nests VAR and Lee–Carter models as special cases and inherits both frameworks’ advantages. A Bayesian estimation approach, adapted from the Minnesota prior, is proposed. The empirical application to the US and French mortality data demonstrates our proposed method’s efficacy in both in-sample and out-of-sample performance.

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A Bayesian Approach to Developing a Stochastic Mortality Model for China

Cited by 15 publications

References 56 publications

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Smooth projection of mortality improvement rates: a Bayesian two-dimensional spline approach

Multiple Population Mortality Jointly Forecasting in China Using PCF Model

Modelling mortality: A bayesian factor-augmented var (favar) approach

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