Bivariate tail estimation: dependence in asymptotic independence

By G. Draisma, H. Drees, Ana Ferre Ira, Laurens de Haan. 2004

169 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2a2e2d1b67620bda6f666a48a4681829e727c9bd

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Citations in the corpus

Hidden regular variation and the rank transform (2005, background): “…to distinguish statistically asymptotic independence from dependence; see Campos et al. (2005), Coles et al. (1999), de Haan and de Ronde (1998), Draisma et al. (2004), Heffernan (2000), Ledford and Tawn (1996), (1997), Maulik and Resnick (2005), Peng (1999), Poon et al. (2003), Resnick (2004),…”
Statistics of Extremes (2004, methodology): “Draisma et al. (2004) show that under certain growth conditions for k = kn, the standardized estimators {Sn(k)}1/2(η̂i − η) (i = 1, 2) and k(η̂i − η) (i = 3, 4) are asymptotically normal with mean 0 and certain asymptotic variances σ 2 i (i = 1, .”
Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case (2008, background): “spectral measure (see Abdous, Ghoudi and Khoudraji (1999) and Einmahl, de Haan and Piterbarg (2001)).”
Copula-Based Measures of Multivariate Association (2010, unknown): “[23] investigate the case of tail independence.”
Estimating the tail-dependence coefficient: Properties and pitfalls (2005, background): “An account on that topic can be found for instance in Draisma et al. (2004) or Falk and Michel (2004). 2 Preliminaries The following approach, discussed by Sibuya (1960) and Joe (1997, p.”
A new class of models for bivariate joint tails (2009, methodology): “…developed one such approach based on a parametric copula that describes dependence throughout the entire domain rather than just the extremes, Draisma et al. (2004) considered an extension of the Ledford and Tawn (1997) model involving a bivariate second-order regular variation condition and…”
Modeling asymptotically independent spatial extremes based on Laplace random fields (2015, methodology): “…independence is therefore plausible, and the right-hand display of Figure 2 shows pairwise estimates of the residual coefficient ρ, calculated as the Hill estimator (Hill, 1975; Draisma et al., 2004) based on the largest k = 40 order statistics of min(X∗(s), X∗(s + ∆s)) for all pairs of sites.”
Non‐parametric Estimation of Tail Dependence (2006, background): “Several estimators for and related tests for tail independence were introduced by Coles et al. (1999), Peng (1999) and Draisma et al. (2004).”