Extreme value properties of multivariate t copulas

By Aristidis K. Nikoloulopoulos, Harry Joe, Haijun Li. 2009

159 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/a976ceb54e6eab8d85b3728e17f53f44f1db4284

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Citations in the corpus

Spatial extremes: Max-stable processes at work (2013, background): “Although closed forms are appreciably more complicated, expressions for the k-variate cumulative distribution functions are given in Nikoloulopoulos et al. (2009).”
Copula-Based Models for Multivariate Discrete Response Data (2013, background): “However, Student t copula can also account for tail dependence in multivariate continuous data [42], whereas MVN copula cannot.”
Space–time modelling of extreme events (2012, methodology): “This paper shows how a pairwise censored likelihood can be used for consistent estimation of the extremes of space-time data under mild mixing conditions, and illustrates this by fitting an extension of a model of Schlather (2002) to hourly rainfall data.”
Gaussian Copula Marginal Regression (2012, unknown): “[41].”
Reparameterizing Marshall–Olkin copulas with applications to sampling (2011, background): “Furthermore, Marshall–Olkin survival copulas appear as the limiting extreme-value copula of multivariate t -copulas as the degrees of freedom tends to 0 14 .”
Tail Risk of Multivariate Regular Variation (2011, methodology): “Unlike λU , however, the tail dependence function provides all the extremal dependence information [29, 18, 26].”
Non-Stationary Dependence Structures for Spatial Extremes (2014, background): “The extremal t model (Nikoloulopoulos et al., 2009; Opitz, 2013; Thibaud and Opitz, 2014) is defined by taking W (s) = 2−df/2+1π1/2 Γ {(df + 1)/2} max{0, ε(s)} (6)”
Tail Dependence for Heavy-Tailed Scale Mixtures of Multivariate Distributions (2009, methodology): “In [18], for example, the tail dependence function of the multivariate t distribution is obtained by (2.”
Tail dependence functions and vine copulas (2010, background): “[19] derived the homogeneous representation of the tail dependence function for d-dimensional t copulas, whose expression depends only on the (d− 1)-dimensional t distribution functions.”
Efficient inference and simulation for elliptical Pareto processes (2013, unknown): “For processes W1 whose correlation function ρα depends on α such that the limit γ(s2 − s1) = limα→∞ α{1 − ρα(s1, s2)} exists and satisfies 0 < γ(s2 − s1) < ∞ for all sites s1, s2 with s1 6= s2, the extremal-t process Z∗ converges to Z∗ BR as α tends to infinity (Nikoloulopoulos et al., 2009; Kabluch”