Non‐parametric Estimation of Tail Dependence

By Rafael Schmidt, Ulrich Stadtmüller. 2006

371 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/94a83faa03a52fde539d953dcf1e4ee446d55bc0

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Citations in the corpus

Probabilistic assessment of flood risks using trivariate copulas (2012, methodology): “2005), and Schmidt–Stadtmüller estimator l U (Schmidt and Stadtmüller 2006).”
Identification of suitable copulas for bivariate frequency analysis of flood peak and flood volume data (2011, methodology): “Although, quantitative estimates of tail dependence are not obtained in this study, such estimators are available in literature and have been employed by Schmidt & Stadtmüller (2006), Poulin et al. (2007), Serinaldi (2008, 2009), and Villarini et al. (2008).”
Copula-Based Measures of Multivariate Association (2010, methodology): “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.”
MAX-STABLE MODELS FOR MULTIVARIATE EXTREMES (2012, background): “The tail copula, R, of Schmidt and Stadtmüller (2006) arises if all d components are required to exceed a large percentile simultaneously: lim n→∞ nPr[F1(X1) > 1− x1/n and . . . and Fd(Xd) > 1− xd/n] = R(x), x ∈ [0,∞)d.”
Robustness of multiple testing procedures against dependence (2009, background): “…testing methods in a genomic setting; Sarkar (2006) obtained new results on false-discovery rates for single-step, multiple testing procedures; Schmidt and Stadtmüller (2006) and Schmidt (2007) discussed uppertailed dependence; and Yekutieli et al. (2006) developed new approaches to the…”
Estimating the tail-dependence coefficient: Properties and pitfalls (2005, background): “Asymptotic normality and strong consistency of λ̂SECU are, e.g., addressed in Schmidt and Stadtmüller (2004).”
Semi‐Parametric Models for the Multivariate Tail Dependence Function – the Asymptotically Dependent Case (2008, background): “The tail dependence function X (x, y) or the stable tail dependence function x +y− X (x, y) can be estimated non-parametrically (see Huang, 1992; Schmidt & Stadtmuller, 2006), or via other measures such as Pickands’ dependence function and spectral measure (see Abdous, Ghoudi & Khoudraji, 1999;…”
Tail asymptotics for the sum of two heavy-tailed dependent risks (2006, background): “Another related refinement of the coefficient λ based on so-called tail copulae is discussed in Schmidt & Stadtmüller [29].”
Analysis of inter-gauge dependence by Kendall’s τK, upper tail dependence coefficient, and 2-copulas with application to rainfall fields (2008, background): “The work investigates also another important property of pairs of random variables, the so-called upper tail dependence coefficient kU (e.g., Joe 1997; Nelsen 1999; Frahm et al. 2005; Schmidt and Stadtmüller 2006).”
Importance of Tail Dependence in Bivariate Frequency Analysis (2007, methodology): “The choice of k is based on the property of homogeneity of the tail copula as stated in Theorem 1 of Schmidt and Stadtmüller 2006 .”
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.”