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Prospective Longevity Risk Analysis

Published online by Cambridge University Press:  10 June 2011

G. Woo ,
C. J. Martin ,
C. Hornsby  and
A. W. Coburn
G. Woo
Affiliation:
Risk Management Solutions, 30 Monument Street, London EC3R 8NB, U.K. Tel: +44 (0) 20 7444 7701; E-mail: Gordon.Woo@rms.com
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Abstract

Mortality improvement has traditionally been analysed using an array of statistical methods, and extrapolated to make actuarial projections. This paper presents a forward-looking approach to longevity risk analysis which is based on stochastic modelling of the underlying causes of mortality improvement, due to changes in lifestyle, health environment, and advances in medical science. The rationale for this approach is similar to that adopted for modelling other types of dynamic insurance risk, e.g. natural catastrophes, where risk analysts construct a stochastic ensemble of events that might happen in the future, rather than rely on a retrospective analysis of the non-stationary and comparatively brief historical record.

Another feature of prospective longevity risk analysis, which is shared with catastrophe risk modelling, is the objective of capturing vulnerability data at a high resolution, to maximise the benefit of detailed modelling capability down to individual risk factor level. Already, the use by insurers of postcode data for U.K. flood risk assessment has carried over to U.K. mortality assessment. Powered by fast numerical computation and parameterised with high quality geographical data, hydrological models of flood risk have superseded the traditional statistical insurance loss models. A decade later, medically-motivated computational models of mortality risk can be expected to gain increasing prominence in longevity risk management.

Keywords

Longevity RiskMortality ImprovementMedicineGeroscienceCatastrophe Modelling
Type
Sessional meetings: papers and abstracts of discussions
Information
British Actuarial Journal , Volume 15 , Issue S1 , 2009 , pp. 235 - 247
Copyright
Copyright © Institute and Faculty of Actuaries 2009

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References

REFERENCES

Bauer, D., Börger, M. & Russ, J. (2010). On the pricing of longevity-linked securities. Insurance Mathematics and Economics, 46(1), 139–149CrossRefGoogle Scholar
Borger, M. (2009). Deterministic shock vs stochastic Value-at-Risk: an analysis of the Solvency II standard model approach to longevity risk. Ulm University workshop presentation.Google Scholar
Cheng, P.C.-H. (2001). Computational models of scientific discovery. In International Encyclopaedia of the Social and Behavioral Sciences, Elsevier Scientific.Google Scholar
CMI (2009). A prototype mortality projections model. Working paper 38.Google Scholar
Embrechts, P., Kluppelberg, C. & Mikosch, T. (1997). Modelling extremal events. Springer, Berlin.CrossRefGoogle Scholar
Future Foundation (2005). Quitting smoking in the 21st century. Report for Nicorette.Google Scholar
JBS 2 (2005). Joint British Societies’ guidelines on prevention of cardiovascular disease in clinical practice. Heart, 91: v1–52.CrossRefGoogle Scholar
Kansas, D. (2009). The end of Wall Street as we know it. Collins Business, New York.Google Scholar
Kornberg, A. (2005). Of serendipity and science. Stanford Medical Magazine, Summer.Google Scholar
Le Fanu, J. (1999). The rise and fall of modern medicine. Little, Brown & Co., London.CrossRefGoogle ScholarPubMed
Levy, D. & Brink, S. (2005). A change of heart: how the people of Framingham, Massachusetts, helped unravel the mysteries of cardiovascular disease. Alfred A. Knopf, New York.Google Scholar
Lichtenberg, F.R. (2005). The impact of new drug launches on longevity: evidence from longitudinal, disease-level data from 52 Countries, 1982–2001. International Journal of Health Care Finance and Economics, 5, 47–73.CrossRefGoogle ScholarPubMed
Martin, C.J., Taylor, P. & Potts, H.W.W. (2008). Construction of an odds model of coronary heart disease using published information: the Cardiovascular Health Improvement Model (CHIME). BMC Medical Informatics and Decision Making 2008, 8:49doi:10.1186/1472–6947–8–49.CrossRefGoogle ScholarPubMed
McPherson, K., Marsh, T. & Brown, M. (2005). Foresight: tackling obesities: future choices — modelling future trends in obesity and the impact on health. Government Office for Science.Google Scholar
Meyers, M.A. (2007). Happy accidents: serendipity in modern medical breakthroughs. Arcade Publishing, New York.Google Scholar
Pitacco, E., Denuit, M., Haberman, S. & Oliverieri, A. (2009). Modelling longevity dynamics for pensions and annuity business. Oxford University Press, Oxford.CrossRefGoogle Scholar
Puddu, P.E. & Menotti, A. (2009). Artificial neural network versus multiple logistic function to predict 25-year coronary heart disease mortality in the Seven Countries Study. European Journal of Cardiovascular Prevention & Rehabilitation 2009, 16.Google ScholarPubMed
Queau, P. (1986). Eloge de la simulation. Seyssel: Éditions du Champ Vallon.Google Scholar
RMS (2010). Managing pandemic risk. http:\\www.rms.comGoogle Scholar
Woo, G. (1999). The mathematics of natural catastrophes. Imperial College Press, London.CrossRefGoogle Scholar