Multivariate extremes, aggregation and dependence in elliptical distributions

By Henrik Hult, Filip Lindskog. Advances in Applied Probability, 2002

304 citations recorded by the source corpus.

Subfields: Finance, Global and Planetary Change, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f559e720a0f0b2867e0f7259cd2fc0bb77893460

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Citations in the corpus

Modelling across extremal dependence classes (2014, background): “The variables X and Y are asymptotically dependent if and only if Q has regularly varying tails (Hult and Lindskog, 2002).”
The Joint Distribution of Stock Returns is Not Elliptical (2010, unknown): “im of this paper is to study in depth the family of elliptical copulas, in particular Student copulas, that have been much used in a ﬁnancial context and indeed have a simple intuitive interpretation [14,18,23,27,30,40]. We investigate in detail whether or not such copulas can faithfully represent t”
Multivariate extremes and the aggregation of dependent risks: examples and counter-examples (2009, unknown): “Sometimes however, spectral measures with respect to other norms are chosen; for instance in Stărică (1999) and Hult and Lindskog (2002), the spectral measure with respect to ‖ · ‖2 and ‖ · ‖∞, respectively, is more convenient in their context.”
The Bivariate Normal Copula (2009, unknown): “This is not the case for Spearman’s rho, cf. (Hult & Lindskog 2002), which is given by: ̺S(̺) := 12 ∫ 1 0 ∫ 1 0 C(u, v; ̺) − uv du dv = 12 ∫ 1 0 ∫ 1 0 C(u, v; ̺) du dv − 3 = 6 π arcsin (̺ 2 ) (6.3) For proofs of (6.3) cf. (Kruskal 1958) or Section 5.3.2 of (Embrechts et al. 2005).”
Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case (2008, background): “…function estimator both theoretically and empirically when the original random vector is modelled by the elliptical random vector Z. Note that in this set-up can be estimated from observations due to the fact that Z is multivariate regularly varying (see Hult & Lindskog, 2002, theorem 4.3).”
The t Copula and Related Copulas (2005, methodology): “Hult & Lindskog (2001) have given a general result for tail dependence in elliptical distribution, and hence its copula.”
Weighted likelihood copula modeling of extreme rainfall events in Connecticut. (2010, background): “…, F−1(up)}, (4) where u = (u1, . . . , up) ∈ (0, 1)p. Properties of metaelliptical copulas have been studied112 by many authors (e.g., Fang et al., 2002; Hult and Lindskog, 2002; Abdous et al., 2005).113 Genest et al. (2007) present detailed analyses and statistical inferences of metaelliptical114…”
Analysis of elliptical copula correlation factor model with Kendall's tau (2013, methodology): “In contrast to Kendall’s tau, however, once we generalize from distributions with Gaussian copulas to the semi-parametric elliptical copula model, Spearman’s rho is no longer invariant within the family of distributions with the same copula correlation matrix [21], that is, a simple relationship ana”
Tail Conditional Expectations for Elliptical Distributions (2003, background): “u2g i1 2 u2 ¢ du = −ψ0(0), if condition ( 13 ) holds.”
Statistics of Extremes (2004, background): “For instance, Hult and Lindskog (2002) study the multivariate extremes of elliptical distributions, focusing in particular on the limiting spectral measure. Alternatively, Capéraà et al (2000) study the class of bivariate copulas given by”