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Statistics seminar: "The Longevity Risk Assessment by Cross Country Common Trends"

Seminario di Statistica

16/05/2013 dalle 16:30 alle 18:00

Dove Dipartimento di Scienze Statistiche - Via Belle Arti 41 - Aula I

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Relatore

Valeria D’Amato

Department of Economics and Statistics, University of Salerno, via Ponte Don Melillo, Campus Universitario, 84084 Fisciano (Salerno), Italy
e-mail: vdamato@unisa.it

Abstract

Recently the interest in the development of country and age-based longevity risk models (D’Amato et al. 2012b, Nijenga et al. 2011, Russolillo et a. 2011, etc.) has been growing. In particular certain factors impacting changes to mortality rates would be expected to be common to certain age- groups, certain cohorts and the entire population (Pitacco, 2007) and other factors are considered common across the countries. The common improvement trends are assessed in respect of “related” population dynamics or “parent” populations characterised by similar socio-economic conditions and eventually also by geographical proximity.
To investigate cross-country longevity common trends, tools to quantify, compare and model the strength of dependence become essential (for instance see Lazar et al. 2009).
The aim of the 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, composed by mortality data from different countries. The proposed algorithm combines model-based predictions in Lee Carter 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. Empirical outcomes are shown by a graphical analysis.

References

Cairns, A.J.G., D. Blake, K. Dowd, G.D. Coughlan, M. Khalaf-Allah, 2011, Bayesian Stochastic Mortality Modelling for Two Populations. ASTIN Bulletin, vol. 41(1): 29–59.

D’Amato V., Haberman S., Russolillo M., 2012a, The Stratified Sampling Bootstrap: an algorithm for measuring the uncertainty in forecast mortality rates in the Poisson Lee -Carter setting, Methodology and Computing in Applied Probability, Volume 14, Issue 1 (2012), Pag. 135-148

D’Amato V., Haberman S., G. Piscopo, Russolillo M., L. Trapani, 2012b, Detecting Longevity Common Trends by Multiple Population Approach, Eight International Longevity Risk and Capital Market Solutions Conference, Waterloo, Ontario Canada

D’Amato V., Di Lorenzo E., Haberman S., Russolillo M., Sibillo M., 2011a, The Poisson log-bilinear Lee Carter model: Applications of efficient bootstrap methods to annuity analyses, North American Actuarial Journal Vol.15, No 15, 315-3
http://www.soa.org/library/journals/north-american-actuarial-journal/2011/no-2/naaj-2011-vol15-no2.aspx

Lahiri, S.N., 2003, Resampling Methods for Dependent Data, Springer.

Lee, R.D., L. R. Carter, 1992, Modelling and Forecasting U.S. Mortality, Journal of the American Statistical Association, 87, 659-671.

Njienga C., Sherris M., 2011, Longevity Risk and the Econometric Analysis of mortality trends and volatility, Asia-Pacific Journal of Risk and Insurance, vol. 5, issue 2.

Pitacco E., 2007, Mortality and Longevity: A Risk Management Perspective, in IAA Life Colloquium, Stockholm

Russolillo, M., G. Giordano, S. Haberman, 2011, Extending the Lee-Carter model: a three-way decomposition. Scandinavian Actuarial Journal, vol. 2011: 2, 96 — 117.