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Stochastic Mortality: The Impact on Target Capital

Published online by Cambridge University Press:  09 August 2013

Annamaria Olivieri
Affiliation:
University of Parma, Faculty and Dept of Economics, Via J.F. Kennedy, 6 – 43100 Parma (Italy), E-Mail: annamaria.olivieri@unipr.it
Ermanno Pitacco
Affiliation:
University of Trieste, Faculty of Economics, Dept of Applied Mathematics, P.le Europa, 1 – 34127 Trieste (Italy), E-Mail: ermanno.pitacco@econ.units.it

Abstract

In this paper, we take the point of view of an insurer dealing with life annuities, which aims at building up a (partial) internal model in order to quantify the impact of mortality risks, namely process and longevity risk, in view of taking appropriate risk management actions. We assume that a life table, providing a best-estimate assessment of annuitants' future mortality is available to the insurer; conversely, the insurer has no access to data sets and the methodology underlying the construction of the life table. Nonetheless, the insurer is aware that, in the presence of mortality risks, a stochastic approach is required. The (projected) life table, which provides a deterministic description of future mortality, should then be used as the basic input of a stochastic model.

The model we propose focuses on the annual number of deaths in a given cohort, which we represent allowing for a random mortality rate. To this purpose, we adopt the widely used Poisson model, first assuming a Gamma-distributed random parameter, and second introducing time-dependence in the parameter itself. Further, we define a Bayesian-inferential procedure for updating the parameters to experience in some situations. The setting we define does not demand advanced analytical tools, while allowing for process and longevity risk in a rigorous way.

The model is then implemented for capital allocation purposes. We investigate the amount of the required capital for a given life annuity portfolio, based on solvency targets which could be adopted within internal models. The outcomes of such an investigation are compared with the capital required according to some standard rules, in particular those proposed within the Solvency 2 project.

Type
Research Article
Copyright
Copyright © International Actuarial Association 2009

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References

Abbink, M. and Saker, M. (2002) Getting to grips with fair value. Presented to the Staple Inn Actuarial Society, March 5. Available at: http://www.sias.org.uk.Google Scholar
Biffis, E. (2005) Affine processes for dynamic mortality and actuarial valuations. Insurance: Mathematics & Economics, 37(3), 443468.Google Scholar
Biffis, E. and Millossovich, P. (2006) A bidimensional approach to mortality risk. Decisions in Economics and Finance, 29, 7194.CrossRefGoogle Scholar
Brémaud, P. (1981) Point processes and queues. Martingale dynamics. Springer-Verlag.Google Scholar
Bühlmann, H. (1970) Mathematical methods in risk theory. Springer-Verlag.Google Scholar
Cairns, A.J.G., Blake, D. and Dowd, K. (2006) Pricing death: frameworks for the valuation and securitization of mortality risk. ASTIN Bulletin, 36(1), 79120.CrossRefGoogle Scholar
Carlin, B.P. and Louis, T.A. (2000) Bayes and empirical Bayes methods for data analysis. Chapman & Hall / CRC.CrossRefGoogle Scholar
CEIOPS (2007) QIS3. Technical Specifications. Part I: Instructions. Available at: http://www.ceiops.eu.Google Scholar
CEIOPS (2008) QIS4. Technical Specifications. Available at: http://www.ceiops.eu.Google Scholar
CMI (2002) An interim basis for adjusting the “92” Series mortality projections for cohort effects. Working Paper 1, The Faculty of Actuaries and Institute of Actuaries.Google Scholar
CMI (2006) Stochastic projection methodologies: Further progress and P-Spline model features, example results and implications. Working Paper 6, The Faculty of Actuaries and Institute of Actuaries.Google Scholar
Dahl, M. (2004) Stochastic mortality in life insurance. Market reserves and mortality-linked insurance contracts. Insurance: Mathematics & Economics, 35(1), 113136.Google Scholar
Dahl, M. and Møller, T. (2006) Valuation and hedging of life insurance liabilities with systematic mortality risk. Insurance: Mathematics & Economics, 39(2), 193217.Google Scholar
Gerber, H.U. (1995) Life insurance mathematics. Springer-Verlag.Google Scholar
Haberman, S. and Olivieri, A. (2008) Risk classification / Life. In: Melnick, E. and Everitt, B., eds, The Encyclopedia of Quantitative Risk Assessment and Analysis. John Wiley & Sons, Chichester, UK, 15351540.Google Scholar
Marocco, P. and Pitacco, E. (1998) Longevity risk and life annuity reinsurance. Transactions of the 26th International Congress of Actuaries, Birmingham, 6, 453479.Google Scholar
Olivier, P. and Jeffrey, T. (2004) Stochastic mortality models. Presentation to the Society of Actuaries in Ireland.Google Scholar
Olivieri, A. (2001) Uncertainty in mortality projections: an actuarial perspective. Insurance: Mathematics & Economics, 29(2), 231245.Google Scholar
Olivieri, A. (2006) Heterogeneity in survival models. Applications to pensions and life annuities. Belgian Actuarial Bulletin, 6, 2339.Google Scholar
Olivieri, A. and Pitacco, E. (2002) Inference about mortality improvements in life annuity portfolios. Transactions of the 27th International Congress of Actuaries, Cancun (Mexico).Google Scholar
Olivieri, A. and Pitacco, E. (2003) Solvency requirements for pension annuities. Journal of Pension Economics & Finance, 2, 127157.Google Scholar
Panjer, H. and Willmot, G.E. (1992) Insurance risk models. The Society of Actuaries.Google Scholar
Pitacco, E. (1992) Risk classification and experience rating in sickness insurance. Transactions of the 24th International Congress of Actuaries, Montréal, Canada, 3, 209221.Google Scholar
Robert, C.D. and Casella, G. (2004) Monte Carlo statistical methods. Springer-Verlag.Google Scholar
Sandström, A. (2006) Solvency. Models, assessment and regulation. Chapman & Hall/CRC.Google Scholar
Smith, A.D. (2005) Stochastic mortality modelling. Talk at the Workshop on the Interface between Quantitative Finance and Insurance, International Centre for Mathematical Science, Edinburgh.Google Scholar
Vaupel, J.W., Manton, K.G. and Stallard, E. (1979) The impact of heterogeneity in individual frailty on the dynamics of mortality. Demography, 16(3), 439454.Google Scholar
Willets, R.C. (2004) The cohort effect: insights and explanations. British Actuarial Journal, 10, 833877.CrossRefGoogle Scholar