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Statistical Analysis of Natural Events in the United States

Published online by Cambridge University Press:  29 August 2014

Charles Levi*
Affiliation:
Compagnie Transcontinentale de Réassurance, Paris
Christian Partrat*
Affiliation:
Institut de Statistique, Université Pierre et Marie Curie, Paris
*
Compagnie Transcontinentale de Réassurance, 15 rue Louis le Grand, 75002 Paris, France.
Institut de Statistique, Université Pierre et Marie Curie, 4 Place Jussieu, 75252 Paris Cedex 05, France.
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Abstract

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A statistical analysis is performed on natural events which can produce important damages to insurers. The analysis is based on hurricanes which have been observed in the United States between 1954 et 1986.

At first, independence between the number and the amount of the losses is examined. Different distributions (Poisson and negative binomial for frequency and exponential, Pareto and lognormal for severity) are tested. Along classical tests as chi-square, Kolmogorov-Smirnov and non parametric tests, a test with weights on the upper tail of the distribution is used: the Anderson – Darling test.

Confidence intervals for the probability of occurrence of a claim and expected frequency for different potential levels of claims are derived. The Poisson Log-normal model gives a very good fit to the data.

Type
Workshop
Copyright
Copyright © International Actuarial Association 1991

References

D'Agostino, R. B. and Stephens, M.A. (1986) Goodness-of-fit techniques. Marcel Dekker.Google Scholar
Friedman, D.G. (1987) U.S. hurricanes and wind storms A technical briefing. Insurance and Reinsurance research group Ltd. London.Google Scholar
Gibbons, J. D. (1971) Non parametric Statistical Inference. Mac Graw Hill.Google Scholar
Hogg, R.V. and Klugman, S.A. (1984) Loss Distributions. Wiley.CrossRefGoogle Scholar
Johnson, N. L. and Kotz, S. (1970) Continuous Univariate Distributions. Houghton.Google Scholar
Patrick, G. (1980) Estimating casualty insurance loss amount distributions. Proceedings of the casualty Actuarial Society. Vol. LXVII.Google Scholar
Ramlau-Hansen, H. (1988) A Solvency study in non-life insurance. Part 1. Analysis of fire, windstorm and glass claims. Scandinavian Actuarial Journal, 334.CrossRefGoogle Scholar
Resnikoff, G.J. and Lieberman, G.J. (1957) Tables of the non-central t-distribution. Stanford University Press.Google Scholar
Tiago de Oliveira, J. (1977) Statistical methodology for large claims. ASTIN Bulletin 9, 19.CrossRefGoogle Scholar
U.S. Department of Commerce (1987) Tropical cyclones of the north atlantic ocean 1871-1986. Historical climatology series, 6–2.Google Scholar
Van Eeden, C. (1961) Some approximations to the percentage points of the non-central i-distribution. Revue de l'Institut International de Statistique 29, 431.CrossRefGoogle Scholar