Modelling dependent data for longevity projections

D’Amato, Valeria; Haberman, Steven; Piscopo, Gabriella; Russolillo, Maria

doi:10.1016/j.insmatheco.2012.09.008

Cited by 40 publications

(20 citation statements)

References 17 publications

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“…Therefore, in this paper we try to develop a more accurate algorithm in terms of prediction intervals. In order to do this, an improved predictor should take into account not only the dependence across age and time (D'Amato et al, 2012b), but also the dependence structure across different populations characterized by similar features, which are potentially affected by common factors.…”

Section: Discussionmentioning

confidence: 99%

“…In order to investigate cross-country common longevity trends, tools to quantify, compare and model the strength of dependence become essential. On one hand, it is necessary to take into account either the dependence for adjacent age groups, or the dependence structure across time in a single population setting: a sort of intradependence structure (D'Amato et al 2012b). On the other hand, the dependence across multiple populations, which we describe as inter-dependence, can be explored for capturing common long run relationships between countries.…”

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confidence: 99%

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Detecting Common Longevity Trends by a Multiple Population Approach

D’Amato

Haberman

Piscopo

et al. 2014

North American Actuarial Journal

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Abstract.Recently the interest in the development of country and longevity risk models (Njienga and Sherris. 2011) has been growing. The investigation of long-run equilibrium relationships could provide valuable information about the factors driving changes in mortality, in particular across ages and across countries. In order to investigate cross-country common longevity trends, tools to quantify, compare and model the strength of dependence become essential. On one hand, it is necessary to take into account either the dependence for adjacent age groups, or the dependence structure across time in a single population setting: a sort of intradependence structure (D'Amato et al. 2012b). On the other hand, the dependence across multiple populations, which we describe as inter-dependence, can be explored for capturing common long run relationships between countries. The objective of our work is to produce longevity projections by taking into account the presence of various forms of cross-sectional and temporal dependencies in the error processes of multiple populations, considering mortality data from different countries. The algorithm that we propose combines model-based predictions in the Lee Carter (LC) framework (1992) with a bootstrap procedure for dependent data, and so both the historical parametric structure and the intra-group error correlation structure are preserved. We modify the model presented by D' Amato et al. (2012b), which applies a sieve bootstrap to the residuals of the LC model and is able to reproduce, in the sampling, the dependence structure of the data under consideration. In the current paper, the algorithm that we build is applied to a pool of populations by using ideas from panel data; we refer to this new algorithm as the Multiple Lee Carter Panel Sieve (MLCPS). We are interested in estimating the relationship between populations of similar socioeconomic conditions. The empirical results show that the MLCPS approach works well in the presence of dependence.

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Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Detecting Common Longevity Trends by a Multiple Population Approach

D’Amato

Haberman

Piscopo

et al. 2014

North American Actuarial Journal

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“…On the other hand, one can also take a model-free approach such as historical simulation, which does not assume any model setting but uses repeated sampling from the historical data (e.g. Liu and Braun, 2010;Coughlan et al, 2011;Li and Ng, 2011;D'Amato et al, 2012). One such technique we test here is the block bootstrap method, which divides the data into overlapping (or non-overlapping) blocks of equal size, draws random samples of blocks (with replacement), and then sequentially lines up these sampled blocks to form pseudo data (e.g.…”

Section: Sensitivity Analysismentioning

confidence: 99%

On the effectiveness of natural hedging for insurance companies and pension plans

Haberman

2015

Insurance: Mathematics and Economics

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Natural hedging is one possible method to reduce longevity risk exposure for an annuity provider or a pension plan. In this paper, we provide an assessment of the effectiveness of natural hedging between annuity and life products, using the correlated Poisson Lee-Carter model, Poisson common factor model, product-ratio model, and historical simulation. Our analysis is based on the mortality experience of UK assured lives, pensioners, and annuitants, and the national population of England and Wales. We consider a range of different scenarios, and find that the level of risk reduction is significant in general, with an average of around 60%. These results have important implications for those insurers, reinsurers, and pension plan sponsors who are seeking ways to hedge their unwanted risk exposures. 2On the effectiveness of natural hedging for insurance companies and pension plans ABSTRACT Natural hedging is one possible method to reduce longevity risk exposure for an annuity provider or a pension plan. In this paper, we provide an assessment of the effectiveness of natural hedging between annuity and life products, using the correlated Poisson Lee-Carter model, Poisson common factor model, product-ratio model, and historical simulation. Our analysis is based on the mortality experience of UK assured lives, pensioners, and annuitants, and the national population of England and Wales. We consider a range of different scenarios, and find that the level of risk reduction is significant in general, with an average of around 60%. These results have important implications for those insurers, reinsurers, and pension plan sponsors who are seeking ways to hedge their unwanted risk exposures.

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“…Some other recent works have been proposed to predict future mortality structures and manage the longevity risk, such as Plat (), Haberman and Renshaw (, , ), Cairns (), D'Amato et al . () and French and O'Hare ().…”

Section: Introductionmentioning

confidence: 99%

“…In D'Amato et al . () the dependence structure of neighboring observations in the population is captured to improve forecasting mortality through the Lee–Carter sieve bootstrap method. In our time series model, by construction, the dependence structure of all mortality rates time series and of all stochastic components that compose the trends of the time series is duly accounted for.…”

Section: Introductionmentioning

confidence: 99%

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

Neves¹,

Fernandes

Veiga

2015

Journal of Forecasting

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In this paper a multivariate time series model using the seemingly unrelated time series equation (SUTSE) framework is proposed to forecast longevity gains. The proposed model is represented in state space form and uses Kalman filtering to estimate the unobservable components and fixed parameters. We apply the model both to male mortality rates in Portugal and the USA. Our results compare favorably, in terms of mean absolute percentage error, in-sample and out-of-sample, to those obtained by the Lee-Carter method and some of its extensions.

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Modelling dependent data for longevity projections

Cited by 40 publications

References 17 publications

Detecting Common Longevity Trends by a Multiple Population Approach

Detecting Common Longevity Trends by a Multiple Population Approach

On the effectiveness of natural hedging for insurance companies and pension plans

Forecasting Longevity Gains Using a Seemingly Unrelated Time Series Model

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