A Directory of Coefficients of Tail Dependence

By Janet E. Heffernan. 2000

161 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d90bf146cebfe2974b3c9fdf4a7dc2df5ec6363d

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Citations in the corpus

Asset Market Linkages in Crisis Periods (2004, background): “However, Heffernan (2001) shows that a large majority of parametric models with asymptotically independent tails are governed by a tail dependence parameter of 1⁄2 .”
Statistics of Extremes (2004, background): “Within the class of asymptotically independent variables, three types of independence can be identified according to the sign of χ̄ = 2η − 1 (Heffernan 2000).”
Bivariate tail estimation: dependence in asymptotic independence (2004, background): “Several other examples were given by Ledford and Tawn (1997) and Heffernan (2000).”
A conditional approach for multivariate extreme values (with discussion) (2004, background): “Similar inconsistencies are found for ηij (see Heffernan (2000)) and for a range of asymptotically derived probability models.”
Copulas: A Review and Recent Developments (2006, background): “It follows that U = lim u↑1 C(u,u) 1 − u , where C(u, v) = Pr U > u,V > v and L = lim u↓0 C(u,u) u Expressions for the coefficient of tail dependence for a wide range of bivariate distributions may be found in Heffernan[66].”
Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications (2004, methodology): “For other examples see Heffernan (2000).”
Hidden regular variation and the rank transform (2005, background): “(2004), Heffernan (2000), Ledford and Tawn (1996), (1997), Maulik and Resnick (2005), Peng (1999), Poon et al.”
Modelling across extremal dependence classes (2014, background): “Heffernan and Tawn (2004) developed a very general modelling assumption that we present in the adapted form of Heffernan and Resnick (2007).”
Tails of correlation mixtures of elliptical copulas (2009, background): “The distinction between tail dependence at asymptotic and subasymptotic levels has already been made several times in the literature [13, 14, 15, 16].”
Empirical estimation of tail dependence using copulas: application to Asian markets (2005, result): “These results constitute a little part of those given by Genest and MacKay (1986a, b), Ledford and Tawn (1996, 1998), Joe (1997), Heffernan (2000) and Caillault and Guégan (2003).”
Characterizations and Examples of Hidden Regular Variation (2004, unknown): “The coefficient of tail dependence is defined by Ledford and Tawn (1996, 1997, 1998) and is a somewhat weaker concept than our definition of hidden regular variation. Schlather (2001) points out some curiosities stemming from the formulation.”
Multivariate non-normally distributed random variables in climate research - introduction to the copula approach (2008, background): “The corresponding coefﬁcient of tail dependence as introduced below provides an asymptotic measure and is a copula property which is invariant under strictly increasing transformations (e.g. Frahm et al., 2005; Coles et al., 1999; Heffernan, 2000).”