The Longevity Risk Assessment by Cross Country Common Trends
:
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
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:
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D’Amato V., Haberman S., Russolillo M., 2012a, The Stratified Sampling Bootstrap: an algorithm for measuring the uncertainty in forecast mortality
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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
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Relatore:
Valeria D’Amato. University of Salerno.
Data:
03/04/2019 - 13:00
Luogo:
[sala 34, 4° piano facoltà scienze statistiche. Città universitaria. h 13.00- 17.00]