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The construction of the claims reserve distribution by means of a semi-Markov backward simulation model

Published online by Cambridge University Press:  09 December 2011

Abstract

The claims reserving problem is currently one of the most debated in actuarial literature. The high level of interest in this topic is due to the fact that Solvency II rules will come into operation in 2014. Indeed, it is expected that quantile computations will be compulsory in the evaluation of company risk and for this reason we think that the construction of the claims reserve random variable distribution assumes a fundamental relevance.

The aim of this paper is to present a method for constructing the claims reserve distribution which can take into account IBNyR (Issued But Not yet Reported) claims in a natural way. The construction of the distribution function for each time of the observed interval is done by means of a Monte Carlo simulation model applied on a backward time semi-Markov process. It should be pointed out that this is the first time that a simulation model based on semi-Markov with backward recurrence time has been presented. The method is totally different from the models given in the current literature.

The most important features given in the paper are:

1) for the first time the Monte Carlo simulation method is applied to a backward semi-Markov environment;

2) the Monte Carlo simulation permits the construction of the random variable of the claims reserve for each year of the studied horizon in a natural way;

3) as already pointed out, the backward process attached to the semi-Markov process permits taking into account the evaluation of the IBNyR claims in a natural way.

In the last part of the paper an applicative example constructed from tables that summarise 4 years of claims from an important Italian insurance company will be given.

Type
Papers
Copyright
Copyright © Institute and Faculty of Actuaries 2011

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References

Ashe, F.R. (1986). An essay at measuring the variance of estimates of outstanding claim payments. ASTIN Bulletin, 16, 99113.Google Scholar
Australian Prudential Regulation Authority (1999). A new statutory solvency standard for general insurers.Google Scholar
Balcer, Y., Sahin, I. (1986). Pension accumulation as a semi-Markov reward process, with applications to pension reform. In J. Janssen Semi-Markov models. Plenum N.Y.Google Scholar
Barbu, V., Boussemart, M., Limnios, N. (2004). Discrete-time semi-Markov model for reliability and survival analysis. Communications in Statistics: Theory and Methods, 33, 28332868.CrossRefGoogle Scholar
Biffi, E., Janssen, J., Manca, R. (2007). Un modello Monte Carlo semi-Markoviano utile alla misura della riserva sinistri. Proceedings of the VIII congresso Nazionale degli Attuari. Trieste.Google Scholar
Biffi, E., D'Amico, G., Di Biase, G., Janssen, J., Manca, R., Silvestrov, D. (2008a). Monte Carlo semi-Markov methods for credit risk migration models and Basel II rules II. Zhurnal Obchyslyuval'no Ta Prykladno I Matematyky, 96, 5986.Google Scholar
Biffi, E., D'Amico, G., Di Biase, G., Janssen, J., Manca, R., Silvestrov, D. (2008b). Monte-Carlo semi-Markov methods for credit risk migration models and Base II rules. I. Zhurnal Obchyslyuval'no Ta Prykladno I Matematyky, 96, 2858.Google Scholar
Bjökwall, S., Hössjer, O., Ohlsson, E. (2009). Non-parametric and parametric bootstrap techniques for age-to-age development factor methods in stochastic claim reserving. Scandinavian Actuarial Journal, 306331.Google Scholar
Bornhuetter, R.L., Ferguson, R.E. (1972). The actuary and IBNR. Proceedings CAS, LIX, 181195.Google Scholar
Christofides, S. (1990). Regression models based on logincremental Payments. In Claims Reserving Manual, 2, Institute of Actuaries, London.Google Scholar
Corradi, G., Janssen, J., Manca, R. (2004). Numerical treatment of homogeneous semi-Markov processes in transient case. Methodology and Computing in Applied Probability, 6, 233246.Google Scholar
D'Amico, G., Guillen, M., Manca, R. (2009). Full backward non-homogeneous semi-Markov processes for disability insurance models: a Catalunya real data application. Insurance: Mathematics and Economics, 45, 173179.Google Scholar
De Alba, E. (2002). Bayesian estimation of outstanding claim reserves. North American Actuarial Journal, 6, 120.Google Scholar
De Dominicis, R., Manca, R. (1984a). Numerical treatment of homogeneous semi-markov processes. Proceedings of the VI meeting “Computer at University”. Dubrovnik, (1984). Available on line at http://dimadefa.eco.uniroma1.it/manca/Numerical_treatment.pdf.Google Scholar
De Dominicis, R., Manca, R. (1984b). An algorithmic approach to non-homogeneous semi-Markov processes. Communications in Statistics Simulation and Computation.Google Scholar
De Medici, G., Janssen, J., Manca, R. (1995). Financial operation evaluation: a semi-Markov approach. Proc. V AFIR Symposium. Brussels.Google Scholar
England, P.D. (2002). Addendum to “Analytic and bootstrap estimates of prediction errors in claims reserving”. Insurance: Mathematics and Economics, 31, 461466.Google Scholar
England, P.D., Verrall, R.J. (1999). Analytic and bootstrap estimates of prediction errors in claims reserving. Insurance: Mathematics and Economics, 25, 281293.Google Scholar
England, P.D., Verrall, R.J. (2002). Stochastic claims reserving in general insurance. British Actuarial Journal, 8, 443510.Google Scholar
England, P.D., Verrall, R.J. (2005). Incorporating expert opinion into a stochastic model for the chain-ladder technique. Insurance: Mathematics and Economics, 37, 355370.Google Scholar
England, P.D., Verrall, R.J. (2006). Predictive distribution of outstanding liabilities in general insurance. Annals of Actuarial Science, 1, 221270.CrossRefGoogle Scholar
Faculty and Institute of Actuaries (1997). Claims Reserving Manual. 2 volumes.Google Scholar
Gigante, P., Sigalotti, L. (2005). Model risk in claims reserving with generalized linear models. Giornale dell'Istituto Italiano degli Attuari, LXVIII, 5587.Google Scholar
Haastrup, S., Arias, E. (1996). Claims reserving in continuous time a non parametric Bayesian approach. ASTIN Bulletin, 26, 139164.CrossRefGoogle Scholar
Haberman, S., Pitacco, E. (1999). Actuarial models for disability insurance. Chapman & Hall.Google Scholar
Hesselager, O. (1994). A Markov model for loss reserving. ASTIN Bulletin, 24, 183193.CrossRefGoogle Scholar
Hoem, J.M. (1972). Inhomogeneous semi-Markov processes, select actuarial tables, and duration-dependence in demography. in T.N.E. Greville, Population, Dynamics. Academic Press, 251296.Google Scholar
Janssen, J. (1966). Application des processus semi-markoviens à un probléme d'invalidité. Bulletin de l'Association Royale des Actuaries Belges, 63, 3552.Google Scholar
Janssen, J., De Dominicis, R. (1984). Finite non-homogeneous semi-Markov processes. Insurance: Mathematics and Economics, 3, 157165.Google Scholar
Janssen, J., Manca, R. (1997). A realistic non-homogeneous stochastic pension funds model on scenario basis. Scandinavian Actuarial Journal, 113137.Google Scholar
Janssen, J., Manca, R. (2006). Applied semi-Markov Processes. Springer Verlag, New York.Google Scholar
Janssen, J., Manca, R. (2007). Semi-Markov risk models for Finance, Insurance and Reliability. Springer, New York, NY, USA, 2007.Google Scholar
Janssen, J., Manca, R. (2009). Outils de construction de modèles internes pour les assurances et les banques. Hermes and Lavoisier, Paris.Google Scholar
Kirschner, G.S., Kerley, C., Isaacs, B. (2002). Two approaches to calculating correlated reserve indications across multiple lines of business. CAS Forum (Fall), 211246.Google Scholar
Levy, P. (1954). Processus semi-Markoviens. Proc. of International Congress of Mathematics, Amsterdam.Google Scholar
Limnios, N., Oprişan, G. (2001). Semi-Markov Processes and Reliability. Birkhauser, Boston.CrossRefGoogle Scholar
Liu, H., Verrall, R.J. (2009). Predictive distributions for reserves which separate true IBNR and IBNeR Claims. ASTIN Bulletin, 39, 3560.CrossRefGoogle Scholar
Mack, T. (1993). Distribution-free calculation of the standard error of chain-ladder reserve estimates. ASTIN Bulletin, 23, 213225.Google Scholar
Mack, T. (1994). Which stochastic model is underlying the chain ladder model? Insurance: Mathematics and Economics, 15, 133138.Google Scholar
Mack, T. (1999). The standard error of chain ladder reserve estimates recursive calculation and inclusion of a tail factor. ASTIN Bulletin, 29, 361366.Google Scholar
Mack, T. (2008). The prediction error of Bornhuetter/Ferguson. ASTIN Bulletin, 38, 87103.CrossRefGoogle Scholar
Merz, M., Wüthrich, M.V. (2006). A credibility approach to the Munich chain-ladder method. Blätter DGVFM, XXVII, 619628.CrossRefGoogle Scholar
Merz, M., Wüthrich, M.V. (2007). Prediction error of the expected claims development result in the chain ladder method. Schweiz. Aktuarver. Mitt., 5, 117137.Google Scholar
Norberg, R. (1993). Prediction of outstanding liabilities in non-life insurance. Astin Bulletin, 23(1), 95115.CrossRefGoogle Scholar
Norberg, R. (1999). Prediction of outstanding claims: Model variations and extensions. Astin Bulletin, 29(1), 525.CrossRefGoogle Scholar
Ntzoufras, I., Dellaportas, P. (2002). Bayesian modelling of outstanding liabilities incorporating claim count uncertainty. North American Actuarial Journal, 6, 113128.CrossRefGoogle Scholar
Peters, G.W., Shevchenko, P.V., Wüthrich, M.V. (2009). Model uncertainty in claim reserving within Tweedie's compound Poisson models. Astin Bulletin, 39, 133.Google Scholar
Pinheiro, P.J.R., Andrade e Silva, J.M., de Lourdes Ceneno, M. (2003). Bootstrap methodology in claim reserving. The Journal of Risk and Insurance, 70, 701714.Google Scholar
Quarg, G., Mack, T. (2004). Munich chain ladder. Blätter DGVFM, XXVI, 597630.CrossRefGoogle Scholar
Renshaw, A.E. (1989). Chain ladder and interactive modelling (claim reserving and GLIM). Journal of the Institute of Actuaries, 116, 559587.Google Scholar
Schmidt, K.D. (2010). A Bibliography on Loss Reserving. Available online at the address http://www.math.tu-dresden.de/sto/schmidt/dsvm/reserve.pdf.Google Scholar
Schnieper, R. (1991). Separating true IBNR and IBNER claims. ASTIN Bulletin, 21, 111127.CrossRefGoogle Scholar
Silvestrov, D.S. (1980). Semi-Markov processes with a discrete state space. Sovetske Radio, Moskow.Google Scholar
Smith, W.L. (1954). Regenerative stochastic processes. Proc. Int. Congr. Math., 2, 304305. Republished in Pro. Roy. Soc. London Ser. A. 232, 6–31 1955.Google Scholar
Stenberg, F., Manca, R., Silvestrov, D. (2006). Semi-Markov reward models for disability insurance. Theory of Stochastic Processes, 12, 239254.Google Scholar
Stenberg, F., Manca, R., Silvestrov, D. (2007). An Algorithmic Approach to Discrete Time Non-Homogeneous Backward Semi-Markov Reward Processes with an Application to Disability Insurance. Methodology and Computing in Applied Probability, 9, 497519.Google Scholar
Takacs, L. (1954). Some investigations concerning recurrent stochastic processes of a certain type. Magyar Tud. Akad. Mat. Kutato Int. KÄozl., 3, 115128.Google Scholar
Taylor, G.C. (2000). Loss reserving: an actuarial perspective. Kluwer.Google Scholar
Taylor, G.C., Ashe, F.R. (1983). Second moments of estimates of outstanding claims. Journal of Econometrics, 23, 3761.Google Scholar
Taylor, G.C., McGuire, G. (2007). A Synchronous bootstrap to account for dependencies between lines of business in the estimation of loss reserve prediction error. North American Actuarial Journal, 11, 7088.Google Scholar
Verrall, R.J. (1989). A state space representation of the chain ladder linear model. Journal of the Institute of Actuaries, 116, 589609.CrossRefGoogle Scholar
Verrall, R.J. (1990). Bayes and empirical Bayes Estimation for the chain ladder model. ASTIN Bulletin, 20, 217243.Google Scholar
Verrall, R.J. (1991). On the estimation of reserves from log linear models. Insurance: Mathematics and Economics, 10, 7580.Google Scholar
Verrall, R.J. (1996). Claims reserving and generalised additive models. Insurance: Mathematics and Economics, 19, 3143.Google Scholar
Verrall, R.J. (2000). An investigation into stochastic claims reserving models and the chain-ladder technique. Insurance: Mathematics and Economics, 26, 9199.Google Scholar
Verrall, R.J. (2004). A Bayesian generalized linear model for the Bornhutter-Ferguson method of claim reserving. North American Actuarial Journal, 8, 6789.Google Scholar
Wright, T.S. (1990). A stochastic method for claim reserving in general insurance. Journal of the Institute of Actuaries, 117, 677731.Google Scholar
Wright, T.S. (1997). Probability distribution of outstanding liability from individual payments data. Claims Reserving Manual 2, Institute of Actuaries, London.Google Scholar
Wüthrich, M.V., Merz, M. (2008). Stochastic claim reserving methods in insurance. Wiley Finance.Google Scholar