LUCIANO, Elisa and VIGNA, Elena (2008): Mortality risk via affine stochastic intensities: calibration and empirical relevance. Published in: belgian actuarial journal , Vol. 8, No. 1 (2008): pp. 5-16.
Preview |
PDF
MPRA_paper_59627.pdf Download (530kB) | Preview |
Abstract
In this paper, we address the mortality risk of individuals and adopt parsimonious time- homogeneous a±ne processes for their mortality intensities. We calibrate the models to different generations in the UK population and investigate their empirical appropriateness. We find that, in spite of their simplicity, non mean reverting processes with deterministic part that increases exponentially - which generalize the Gompertz law - seem to be appropriate descriptors of human mortality. The proposed models prove to fulfill most of the properties that a good model for stochastic mortality should have. Empirical results show that the generalization is worth explor- ing. Indeed, the variability of number of deaths may increase considerably due to the randomness of the mortality intensity. We show that the models are suitable for mortality forecasting and mortality trend assessment.
Item Type: | MPRA Paper |
---|---|
Original Title: | Mortality risk via affine stochastic intensities: calibration and empirical relevance |
Language: | English |
Keywords: | stochastic mortality, a±ne processes, survival probability modeling, survival proba- bility calibration. |
Subjects: | G - Financial Economics > G2 - Financial Institutions and Services > G22 - Insurance ; Insurance Companies ; Actuarial Studies J - Labor and Demographic Economics > J1 - Demographic Economics > J11 - Demographic Trends, Macroeconomic Effects, and Forecasts |
Item ID: | 59627 |
Depositing User: | Prof.Dr. Elisa LUCIANO |
Date Deposited: | 04 Nov 2014 05:37 |
Last Modified: | 27 Sep 2019 09:51 |
References: | Arnold, L. (1974). Stochastic Differentials Equations: Theory and Applications, John Wiley and Sons. Biffis, E. (2005). A±ne processes for dynamic mortality and actuarial valuations, Insurance: Mathematics and Economics 37: 443-468. Bowers, N. L., Gerber, H. U., Hickman, Jones, J. C. and Nesbitt, C. J. (1986). Actuarial Mathematics, The Society of Actuaries, Itaca. Bremaud, P. (1981). Point Processes and Queues,Martingale Dynamics, Springer Verlag, New York. Brouhns, N., Denuit, M. and Vermunt, J. K. (2002). A poisson log-bilinear approach to the construction of projected lifetables, Insurance: Mathematics and Economics 31: 373-393. Cairns, A. J. G., Blake, D. and Dowd, K. (2006). Pricing death: Framework for the valuation and securitization of mortality risk, ASTIN Bulletin 36: 79-120. Dahl, M. (2004). Stochastic mortality in life insurance: market reserves and mortality-linked insurance contracts, Insurance: Mathematics and Economics 35: 113-136. Denuit, M. and Devolder, P. (2006). Continuous time stochastic mortality and securitization of longevity risk, Working Paper 06-02, Institut des Sciences Actuarielles, Universite' Catholique de Louvain, Louvain-la-Neuve. Duffie, D. (2001). Dynamic Asset Pricing Theory, Third Edition, Princeton University Press. Duffie, D. and Singleton, K. J. (2003). Credit risk, Princeton University Press. Duffie, D., Filipovic, D. and Schachermayer, W. (2003). Affine processes and applications in Finance, Annals of Applied Probability 13: 984-1053. Duffie, D., Pan, J. and Singleton, K. (2000). Transform analysis and asset pricing for affine jump-diffusions, Econometrica 68: 1343-1376. Feller, W. (1951). Two singular diffusion problems, The Annals of Mathematics 54: 173-182. Gerber, H. U. (1997). Life Insurance Mathematics, Springer Verlag, Berlin. Institute and Faculty of Actuaries (1990). Standard Tables of Mortality: the "92" Series, The Institute of Actuaries and the Faculty of Actuaries. Lee, R. D. (2000). The Lee-Carter method for forecasting mortality, its various extensions and applications, North American Actuarial Journal 4: 80-93. Lee, R. D. and Carter, L. R. (1992). Modelling and forecasting U.S. mortality, Journal of the American Statistical Association 87: 659-675. Luciano, E. and Vigna, E. (2005). Non mean reverting affine processes for stochastic mortality, Carlo Alberto Notebook 30/06 and ICER WP 4/05. Luciano, E., Spreeuw, J. and Vigna, E. (2008). Modeling stochastic mortality for dependent lives,Insurance: Mathematics and Economics 43: 234-244. Menoncin, F. (2008). The role of longevity bonds in optimal portfolios, Insurance: Mathematics and Economics 42: 343-358. Milevsky, M. and Promislow, S. D. (2001). Mortality derivatives and the option to annuitise, Insurance: Mathematics and Economics 29: 299-318. Pitacco, E. (2004a). Longevity risk in living benefits, in E. Fornero and E. Luciano (eds), Developing an Annuity Market in Europe, Edward Elgar, Cheltenham, pp. 132-167. Pitacco, E. (2004b). Survival models in a dynamic context: a survey, Insurance: Mathematics and Economics 35: 279-298. Renshaw, A. E. and Haberman, S. (2003). On the forecasting of mortality reductions factors,Insurance: Mathematics and Economics 32: 379-401. Schrager, D. (2006). Affine stochastic mortality, Insurance: Mathematics and Economics 38: 81-97. Thatcher, A. R. (1999). The long-term pattern of adult mortality and the highest attained age, Journal of the Royal Statistical Society A 162: 5-43. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany) (2002). Human Mortality Database, Available at www.mortality.org or www.humanmortality.de. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/59627 |