Orthant tail dependence of multivariate extreme value distributions

The orthant tail dependence describes the relative deviation of upper- (or lower-) orthant tail probabilities of a random vector from similar orthant tail probabilities of a subset of its components, and can be used in the study of dependence among extreme values. Using the conditional approach, this paper examines the extremal dependence properties of multivariate extreme value distributions and their scale mixtures, and derives the explicit expressions of orthant tail dependence parameters for these distributions. Properties of the tail dependence parameters, including their relations with other extremal dependence measures used in the literature, are discussed. Various examples involving multivariate exponential, multivariate logistic distributions and copulas of Archimedean type are presented to illustrate the results.

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

[2]  R. Nelsen An Introduction to Copulas (Springer Series in Statistics) , 2006 .

[3]  T. A. Buishand,et al.  Bivariate extreme-value data and the station-year method , 1984 .

[4]  Haijun Li,et al.  Tail Dependence for Heavy-Tailed Scale Mixtures of Multivariate Distributions , 2009, Journal of Applied Probability.

[5]  C. Genest,et al.  A characterization of gumbel's family of extreme value distributions , 1989 .

[6]  Susan H. Xu,et al.  On the dependence structure and bounds of correlated parallel queues and their applications to synchronized stochastic systems , 2000, Journal of Applied Probability.

[7]  N. L. Johnson,et al.  Continuous Multivariate Distributions, Volume 1: Models and Applications , 2019 .

[8]  Haijun Li Tail Dependence of Multivariate Pareto Distributions , 2006 .

[9]  S. Rachev Handbook of heavy tailed distributions in finance , 2003 .

[10]  Rafael Schmidt,et al.  Tail dependence for elliptically contoured distributions , 2002, Math. Methods Oper. Res..

[11]  P. Embrechts,et al.  Chapter 8 – Modelling Dependence with Copulas and Applications to Risk Management , 2003 .

[12]  J. Shanthikumar,et al.  Multivariate Stochastic Orders , 2007 .

[13]  Harry Joe,et al.  Multivariate Distributions from Mixtures of Max-Infinitely Divisible Distributions , 1996 .

[14]  R. Nelsen An Introduction to Copulas , 1998 .

[15]  Helena Ferreira,et al.  Measuring the extremal dependence , 2005 .

[16]  I. Olkin,et al.  A Multivariate Exponential Distribution , 1967 .

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

[18]  K. Joag-dev,et al.  Negative Association of Random Variables with Applications , 1983 .

[19]  I. Olkin,et al.  Families of Multivariate Distributions , 1988 .

[20]  Holger Rootzén,et al.  Extreme Values in Finance, Telecommunications, and the Environment , 2003 .

[21]  N. Tajvidi,et al.  Multivariate Generalized Pareto Distributions , 2006 .

[22]  Janet E. Heffernan,et al.  A conditional approach for multivariate extreme values , 2004 .

[23]  Haijun Li,et al.  Tail Dependence Comparison of Survival Marshall–Olkin Copulas , 2008 .

[24]  Haijun Li,et al.  Tail dependence for multivariate t -copulas and its monotonicity , 2008 .

[25]  N. Ng,et al.  Extreme values of ζ′(ρ) , 2007, 0706.1765.

[26]  Stefan Straetmans,et al.  Banking System Stability: A Cross-Atlantic Perspective , 2005, SSRN Electronic Journal.

[27]  H. Joe Multivariate models and dependence concepts , 1998 .

[28]  Friedrich Schmid,et al.  Multivariate conditional versions of Spearman's rho and related measures of tail dependence , 2007 .

[29]  Jonathan A. Tawn,et al.  A conditional approach for multivariate extreme values (with discussion) , 2004 .

[30]  Janet E. Heffernan,et al.  Dependence Measures for Extreme Value Analyses , 1999 .

[31]  Claudia Klüppelberg,et al.  Multivariate Tail Copula: Modeling and Estimation , 2006 .