Lower Tail Dependence for Archimedean Copulas: Characterizations and Pitfalls

Tail dependence copulas provide a natural perspective from which one can study the dependence in the tail of a multivariate distribution.For Archimedean copulas with continuously differentiable generators, regular variation of the generator near the origin is known to be closely connected to convergence of the corresponding lower tail dependence copulas to the Clayton copula.In this paper, these characterizations are refined and extended to the case of generators which are not necessarily continuously differentiable.Moreover, a counterexample is constructed showing that even if the generator of a strict Archimedean copula is continuously differentiable and slowly varying at the origin, then the lower tail dependence copulas do not need to converge to the independent copula.

[1]  C. Genest,et al.  Statistical Inference Procedures for Bivariate Archimedean Copulas , 1993 .

[2]  Christian Genest,et al.  Copules archimédiennes et families de lois bidimensionnelles dont les marges sont données , 1986 .

[3]  Emiliano A. Valdez,et al.  Understanding Relationships Using Copulas , 1998 .

[4]  D. Oakes,et al.  On the preservation of copula structure under truncation , 2005 .

[5]  M. Meerschaert Regular Variation in R k , 1988 .

[6]  A. Juri,et al.  Copula convergence theorems for tail events , 2002 .

[7]  Marco Scarsini,et al.  Archimedean copulae and positive dependence , 2005 .

[8]  A. Juri,et al.  Limiting dependence structures for tail events, with applications to credit derivatives , 2006, Journal of Applied Probability.

[9]  C. Kimberling A probabilistic interpretation of complete monotonicity , 1974 .

[10]  N. Bäuerle,et al.  Modeling and Comparing Dependencies in Multivariate Risk Portfolios , 1998, ASTIN Bulletin.

[11]  Bill Ravens,et al.  An Introduction to Copulas , 2000, Technometrics.

[12]  Michel Denuit,et al.  Actuarial Theory for Dependent Risks: Measures, Orders and Models , 2005 .

[13]  Stuart A. Klugman,et al.  Fitting bivariate loss distributions with copulas , 1999 .

[14]  Convergence of Archimedean Copulas , 2008 .

[15]  Mario V. Wüthrich,et al.  Tail Dependence from a Distributional Point of View , 2003 .

[16]  Fabio Spizzichino,et al.  Bivariate survival models with Clayton aging functions , 2005 .