Estimating the tail-dependence coefficient: Properties and pitfalls

By Gabriel Frahm, Markus Junker, Rafael Schmidt. 2005

362 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/16655b0476ff4695cd92277a8a79026bd15d3cc7

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Citations in the corpus

Bivariate flood frequency analysis using the copula function: a case study of the Litija station on the Sava River (2015, methodology): “Frahm et al. (2005) introduced the upper tail dependence estimator, whichwas also used by Poulin et al. (2007) and Ganguli and Reddy (2013).”
Copula-Based Measures of Multivariate Association (2010, methodology): “λL,n,k have been investigated by several authors using techniques from extreme value theory; we mention Huang [52], Ledford and Tawn [70], Dobri´ c and Schmid [20], Frahm et al. [ 31 ], and Schmidt and Stadtmuller [98].”
Derivation of low flow distribution functions using copulas (2014, neutral): “The most common definition of tail dependence between two random variables X and Y is (Frahm et al., 2005): kU 1⁄4 lim u!1 PfGXðxÞ > u HY ðyÞ > u j g kL 1⁄4 lim u!0þ PfGXðxÞ 6 u HYðyÞ 6 u j g ð2Þ”
Spatio-temporal analysis and derivation of copula-based intensity–area–frequency curves for droughts in western Rajasthan (India) (2013, methodology): “In this study, Capéraá–Fougéres–Genest (CFG estimator k U ; Capéraá et al. 1997; Frahm et al. 2005) estimator is employed to study non-parametric TDC.”
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].”
Absolutely Continuous Copulas with Given Diagonal Sections (2008, background): “…tail dependence coefficients) have been largely considered in several financial applications: see, for example, Albrecher et al. (2006), Charpentier and Segers (2007), Frahm et al. (2005), Juri and Wüthrich (2002), Malevergne and Sornette (2006), Schmidt and Stadtmüller (2006), and Zhang (2008).”
A comparative copula‐based bivariate frequency analysis of observed and simulated storm events: A case study on Bartlett‐Lewis modeled rainfall (2011, background): “…obvious that when the copula is not fitted well in the upper right tail, the results of the frequency analysis will be unreliable: It is therefore of major importance to properly model the dependence of extremes, or tail dependence [Salvadori et al., 2007; Poulin et al., 2007; Frahm et al., 2005].”
Multivariate modeling of droughts using copulas and meta-heuristic methods (2014, background): “The coefficient of upper tail dependence is defined (Frahm et al. 2005) as, kU 1⁄4 limt!1 FX x ð Þ[ t f jFY y ð Þ[ tg: If kU 2 0; 1 ð , then Fx(x) and Fy(y) are said to show the upper tail dependence or extremal dependence in the upper tail.”
Uncertainty analysis of bias from satellite rainfall estimates using copula method (2014, background): “However, since the Gaussian copula does not have upper or lower tail dependence, it may not be a suitable choice for modeling the dependencies of extremes (Schmidt, 2005; Frahm et al, 2005; Schmidt and Stadtmuller, 2006; Serinaldi, 2009a,b; Aghakouchak, 2010).”
Bivariate drought frequency analysis using the copula method (2012, methodology): “Although the underlying assumption is that the copula can be approximated using an extreme value copula, Frahm et al. (2005) have shown that this assumption is not strong and that the estimator performs well even when the copula does not belong to the extreme value class (Serinaldi 2008).”