Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case

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Tail dependence functions and vine copulas (2010, methodology): “[13], we focus in this paper on both tail dependence and conditional tail dependence functions as a unified tool for extremal dependence analysis.”
Copula-Based Measures of Multivariate Association (2010, background): “discussed in Huang [52], Schmidt and Stadtmuller [98], de Haan et al. [17], Einmahl et al. [24], Kluppelberg et al. [ 64 ], and Joe et al. [59], see also reference therein.”
Extreme-value copulas (2009, background): “See for instance [5, 13, 49, 24, 59, 60, 61, 63] for some (semi-)parametric approaches and [1, 6, 23, 22, 25, 81] for some nonparametric ones.”
Tail Risk of Multivariate Regular Variation (2011, background): “In fact, various upper tail dependence parameters can be represented by the upper tail dependence function ( Kluppelberg et al. 2008; Nikoloulopoulos et al. 2009; Joe et al. 2010), defined as follows,”
Analysis of elliptical copula correlation factor model with Kendall's tau (2013, unknown): “A popular model for high dimensional data is the semi-parametric elliptical copula model [13, 23, 24, 29], the family of distributions whose dependence structures are specified by parametric elliptical copulas but whose marginal distributions are left unspecified.”