Phantoms Never Die: Living with Unreliable Population Data

Cairns, Andrew J. G.; Blake, David; Dowd, Kevin; Kessler, Amy

doi:10.2139/ssrn.2676648

Cited by 11 publications

(17 citation statements)

References 33 publications

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“…6.3.6. Restatement risk concerns the possibility that official estimates of the national population or death counts might be revised up or down, with potential impacts on index-based hedge payoffs (Cairns et al ., 2016). Restatements will often impact on previously stated mortality rates (especially following a decennial census).…”

Section: Index Versus Customised Hedges and Basis Riskmentioning

confidence: 99%

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Still living with mortality: the longevity risk transfer market after one decade

Blake¹,

Cairns²,

Dowd³

et al. 2019

Br. Actuar. J.

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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 solutions – buy-outs, buy-ins and longevity insurance – have triumphed over capital markets solutions that were expected to dominate at the time. Some capital markets solutions – longevity-spread bonds, longevity swaps, q-forwards and tail-risk protection – have 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 examined – such as longevity-linked securities and reinsurance sidecars.

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Section: Index Versus Customised Hedges and Basis Riskmentioning

confidence: 99%

“…The future risks and impacts of such restatements can be assessed through use of the same methodology for identifying phantoms proposed in Cairns et al . (2016).…”

Section: Index Versus Customised Hedges and Basis Riskmentioning

confidence: 99%

Still living with mortality: the longevity risk transfer market after one decade

Blake¹,

Cairns²,

Dowd³

et al. 2019

Br. Actuar. J.

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“…Future research warrants an analysis of the quality of the Chinese mortality data set, drawing on Cairns et al . ().…”

Section: Resultsmentioning

confidence: 99%

“…Finally, we note that the issue of data quality is beyond the scope of this paper. Future research warrants an analysis of the quality of the Chinese mortality data set, drawing on Cairns et al (2016).…”

Section: Resultsmentioning

confidence: 99%

A Bayesian Approach to Developing a Stochastic Mortality Model for China

Li¹,

Zhou

Zhu

et al. 2019

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

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Summary Stochastic mortality models have a wide range of applications. They are particularly important for analysing Chinese mortality, which is subject to rapid and uncertain changes. However, owing to data‐related problems, stochastic modelling of Chinese mortality has not been given adequate attention. We attempt to use a Bayesian approach to model the evolution of Chinese mortality over time, taking into account all of the problems associated with the data set. We build on the Gaussian state space formulation of the Lee–Carter model, introducing new features to handle the missing data points, to acknowledge the fact that the data are obtained from different sources and to mitigate the erratic behaviour of the parameter estimates that arises from the data limitations. The approach proposed yields stochastic mortality forecasts that are in line with both the trend and the variation of the historical observations. We further use simulated pseudodata sets with resembling limitations to validate the approach. The validation result confirms our approach's success in dealing with the limitations of the Chinese mortality data.

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“…For the England and Wales population, Cairns et al . (2016) finds evidence to suggest that the anomalies observed for the 1919 and 1920 cohorts are due to unusual pattern of births and the methodology employed to estimate mid-year population, which is related to the First World War. A mild kink also appears around the generation born in 1900 for the US population.…”

Section: Empirical Studiesmentioning

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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Phantoms Never Die: Living with Unreliable Population Data

Cited by 11 publications

References 33 publications

Still living with mortality: the longevity risk transfer market after one decade

Still living with mortality: the longevity risk transfer market after one decade

A Bayesian Approach to Developing a Stochastic Mortality Model for China

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

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