Extreme dependence of multivariate catastrophic losses

Natural catastrophes cause insurance losses in several different lines of business. An approach to modelling the dependence in loss severities is to assume that they are related to the intensity of the natural disaster. In this paper we introduce a factor model and investigate the extreme dependence. We derive a specific extreme dependence structure when considering an heavy-tailed intensity. Estimation procedures are presented and their moderate sample properties are compared in a simulation study. We also motivate our approach by an illustrative example from storm insurance.

[1]  Lars-Gunnar Benckert,et al.  Statistical Models of Claim Distributions in Fire Insurance , 1974, ASTIN Bulletin.

[2]  Christian Genest,et al.  A nonparametric estimation procedure for bivariate extreme value copulas , 1997 .

[3]  S. Resnick Extreme Values, Regular Variation, and Point Processes , 1987 .

[4]  Hélène Cossette,et al.  Modeling Catastrophes and their Impact on Insurance Portfolios , 2003 .

[5]  Joint exceedances of the ARCH process , 2004, Journal of Applied Probability.

[6]  Jonathan A. Tawn,et al.  Bivariate extreme value theory: Models and estimation , 1988 .

[7]  J. Tawn Modelling multivariate extreme value distributions , 1990 .

[8]  P. Hall,et al.  Prediction Regions for Bivariate Extreme Events , 2004 .

[9]  J. Angus The Asymptotic Theory of Extreme Order Statistics , 1990 .

[10]  Jonathan A. Tawn,et al.  Modelling Dependence within Joint Tail Regions , 1997 .

[11]  A. McNeil Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory , 1997, ASTIN Bulletin.

[12]  J. Teugels,et al.  Practical Analysis of Extreme Values , 1996 .

[13]  P. Hall,et al.  Distribution and dependence-function estimation for bivariate extreme-value distributions , 2000 .

[14]  Jan Beirlant,et al.  Estimating catastrophic quantile levels for heavy-tailed distributions , 2004 .

[15]  S. Coles,et al.  Modelling Extreme Multivariate Events , 1991 .

[16]  H. Joe Multivariate extreme value distributions , 1997 .

[17]  L. Breiman,et al.  On Some Limit Theorems Similar to the Arc-Sin Law , 1965 .

[18]  Harry Joe,et al.  Bivariate Threshold Methods for Extremes , 1992 .

[19]  A. McNeil,et al.  Common Poisson Shock Models: Applications to Insurance and Credit Risk Modelling , 2003, ASTIN Bulletin.

[20]  Sidney I. Resnick,et al.  Estimating the limit distribution of multivariate extremes , 1993 .

[21]  A. Ledford,et al.  Statistics for near independence in multivariate extreme values , 1996 .

[22]  L. de Haan,et al.  Estimating the spectral measure of an extreme value distribution , 1997 .

[23]  J. Hüsler Maxima of normal random vectors: between independence and complete dependence , 1989 .

[24]  H. Block Multivariate Exponential Distribution , 2006 .

[25]  Rafael Schmidt,et al.  Non‐parametric Estimation of Tail Dependence , 2006 .

[26]  L. de Haan,et al.  Estimating the probability of a rare event , 1999 .

[27]  Laurens de Haan,et al.  Estimating a multidimensional extreme-value distribution , 1993 .

[28]  Paul Deheuvels,et al.  On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions , 1991 .

[29]  Satishs Iyengar,et al.  Multivariate Models and Dependence Concepts , 1998 .

[30]  L. Haan,et al.  Nonparametric estimation of the spectral measure of an extreme value distribution , 2001 .

[31]  C. Klüppelberg,et al.  Modelling Extremal Events , 1997 .

[32]  L. de Haan,et al.  Bivariate tail estimation: dependence in asymptotic independence , 2004 .

[33]  Johan Segers,et al.  Statistics of Multivariate Extremes , 2005 .

[34]  U. Stadtmüller,et al.  Generalized regular variation of second order , 1996, Journal of the Australian Mathematical Society. Series A. Pure Mathematics and Statistics.

[35]  Claudia Klüppelberg,et al.  Dependence Estimation and Visualization in Multivariate Extremes with Applications to Financial Data , 2004 .

[36]  Janos Galambos,et al.  Order Statistics of Samples from Multivariate Distributions , 1975 .

[37]  S. Resnick,et al.  Consistency of Hill's estimator for dependent data , 1995, Journal of Applied Probability.

[38]  J. Einmahl The almost sure behavior of the weighted empirical process and the LIL for the weighted tail empirical process , 1992 .

[39]  J. Corcoran Modelling Extremal Events for Insurance and Finance , 2002 .

[40]  Jonathan A. Tawn,et al.  Statistical Methods for Multivariate Extremes: An Application to Structural Design , 1994 .