Kybernetika 48 no. 5, 988-1006, 2012

On extremal dependence of block vectors

Helena Ferreira and Marta Ferreira

Abstract:

Due to globalization and relaxed market regulation, we have assisted to an increasing of extremal dependence in international markets. As a consequence, several measures of tail dependence have been stated in literature in recent years, based on multivariate extreme-value theory. In this paper we present a tail dependence function and an extremal coefficient of dependence between two random vectors that extend existing ones. We shall see that in weakening the usual required dependence allows to assess the amount of dependence in $d$-variate random vectors based on bidimensional techniques. Simple estimators will be stated and can be applied to the well-known \emph{stable tail dependence function}. Asymptotic normality and strong consistency will be derived too. An application to financial markets will be presented at the end.

Keywords:

tail dependence, multivariate extreme value theory, extremal coefficients

Classification:

60G70

References:

  1. J. Beirlant, Y. Goegebeur, J. Segers and J. Teugels: Statistics of Extremes: Theory and Application. John Wiley, Chichester 2004.   CrossRef
  2. S. Coles, J. Heffernan and J. Tawn: Dependence measures for extreme value analysis. Extremes 2 (1999), 339-366.   CrossRef
  3. G. Draisma, H. Drees, A. Ferreira and L. de Haan: Bivariate tail estimation: dependence in asymptotic independence. Bernoulli 10 (2004), 251-280.   CrossRef
  4. H. Drees and P. Müller: Fitting and validation of a bivariate model for large claims. Insurance Math. Econom. 42 (2008), 638-650.   CrossRef
  5. P. Embrechts, F. Lindskog and A. McNeil: Modelling dependence with copulas and applications to risk management. In: Handbook of Heavy Tailed Distibutions in Finance (S. Rachev, ed.), Elsevier, Amsterdam 2003, pp. 329-384.   CrossRef
  6. J. D. Fermanian, D. Radulović and M. Wegkamp: Weak convergence of empirical copula processes. Bernoulli 10 (2004), 5, 847-860.   CrossRef
  7. G. Frahm: On the extremal dependence coefficient of multivariate distributions. Statist. Probab. Lett. 76 (2006), 1470-1481.   CrossRef
  8. G. Frahm, M. Junker and R. Schmidt: Estimating the tail-dependence coefficient: properties and pitfalls. Insurance Math. Econom. 37 (2005), 1, 80-100.   CrossRef
  9. D. Gilat and T. Hill: One-sided refinements of the strong law of large numbers and the Glivenko-Cantelli theorem. Ann. Probab. 20 (1992), 1213-1221.   CrossRef
  10. L. Hua and H. Joe: Tail order and intermediate tail dependence of multivariate copulas. J. Multivariate Anal. 102 (2011), 10, 1454-1471.   CrossRef
  11. X. Huang: Statistics of Bivariate Extreme Values. Ph.D. Thesis, Tinbergen Institute Research Series 22, Erasmus University Rotterdam 1992.   CrossRef
  12. H. Joe: Multivariate Models and Dependence Concepts. Chapman and Hall, London 1997.   CrossRef
  13. A. Krajina: An M-Estimator of Multivariate Tail Dependence. Tilburg University Press 2010.   CrossRef
  14. A. Ledford and J. Tawn: Statistics for near independence in multivariate extreme values. Biometrika 83 (1996), 169-187.   CrossRef
  15. A. Ledford and J. Tawn: Modelling Dependence within joint tail regions. J. R. Statist. Soc. Ser. B Stat. Methodol. 59 (1997), 475-499.   CrossRef
  16. H. Li: Tail Dependence of Multivariate Pareto Distributions. WSU Mathematics Technical Report 2006-6, Washington 2006.   CrossRef
  17. H. Li: Tail dependence comparison of survival Marshall-Olkin copulas. Methodol. Comput. Appl. Probab. 10 (2008), 1, 39-54.   CrossRef
  18. H. Li: Orthant tail dependence of multivariate extreme value distributions. J. Multivariate Anal. 100 (2009), 1, 243-256.   CrossRef
  19. H. Li and Y. Sun: Tail dependence for heavy-tailed scale mixtures of multivariate distributions. J. Appl. Probab. 46 (2009), 4, 925-937.   CrossRef
  20. A. W. Marshall and I. Olkin: A multivariate exponential distribution. J. Amer. Statist. Assoc. 62 (1967), 30-44.   CrossRef
  21. R. B. Nelsen: Nonparametric measures of multivariate association. In: Distribution with fixed marginals and related topics, IMS Lecture Notes - Monograph Series, Vol. 28 (L. Rüschendorf et al., eds.) Hayward, Institute of Mathematical Statistics 1996, pp. 223-232.   CrossRef
  22. R. B. Nelsen: An Introduction to Copulas. Second edition. Springer, New York 2006.   CrossRef
  23. G. Neuhaus: On the weak convergence of stochastic processes with multidimensional time parameter. Ann. Math. Statist. 42 (1971), 1285-1295.   CrossRef
  24. F. Schmid and R. Schmidt: Multivariate conditional versions of Spearman's rho and related measures of tail dependence. J. Multivariate Anal. 98 (2007), 1123-1140.   CrossRef
  25. R. Schmidt and U. Stadtmüller: Nonparametric estimation of tail dependence. Scand. J. Statist. 33 (2006), 307-335.   CrossRef
  26. R. L. Smith: Max-stable processes and spatial extremes. Preprint, Univ. North Carolina, USA 1990.   CrossRef
  27. R. L. Smith and I. Weissman: Characterization and estimation of the multivariate extremal index. Manuscript, UNC 1996.   CrossRef
  28. M. Sibuya: Bivariate extreme statistics. Ann. Inst. Statist. Math. 11 (1960), 195-210.   CrossRef
  29. J. Tiago de Oliveira: Structure theory of bivariate extremes: extensions. Est. Mat. Estat. e Econ. 7 (1962/63), 165-195.   CrossRef
  30. E. F. Wolff: N-dimensional measures of dependence. Stochastica 4 (1980), 3, 175-188.   CrossRef