Extreme-value copulas
By Gordon Gudendorf, Johan Segers. 2009
267 citations recorded by the source corpus.
Being the limits of copulas of componentwise maxima in independent random samples, extreme-value copulas can be considered to provide appropriate models for the dependence structure between rare events. Extreme-value copulas not only arise naturally in the domain of extreme-value theory, they can also be a convenient choice to model general positive dependence structures. The aim of this survey is to present the reader with the state-of-the-art in dependence modeling via extreme-value copulas. Both probabilistic and statistical issues are reviewed, in a nonparametric as well as a parametric context.
Subfields: Finance, Global and Planetary Change, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/1c0d4b454bf0ed636019dc7949030780795b4e8b
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Finance
- Martin Ruppert
- Sandra Gaißer
- Ivan Kojadinovic
- Jun Yan
- Johan Segers
- Friedrich Schmid
- Rafael Schmidt
- Thomas Blumentritt
In Global and Planetary Change
- Jun Yan
- Ivan Kojadinovic
- Johan Segers
- Friedrich Schmid
- Martin Ruppert
- Sandra Gaißer
- Rafael Schmidt
- L. Hamm
In Statistics and Probability
- Martin Ruppert
- Sandra Gaißer
- Ivan Kojadinovic
- Friedrich Schmid
- Jun Yan
- Rafael Schmidt
- Thomas Blumentritt
- Gordon Gudendorf
Citations in the corpus
- MAX-STABLE MODELS FOR MULTIVARIATE EXTREMES (2012, background): “An extensive survey of the literature on extreme-value copulas is given in Gudendorf and Segers (2010).”
- An Overview of the Goodness-of-Fit Test Problem for Copulas (2012, unknown): “Extreme-value copulas are important in a lot of fields because they characteriz e the large-sample limits of copulas of componentwise maxima of strongly mixing st ationary sequences ([26], [53], and the recent survey [50]).”
- An event-based approach for extreme joint probabilities of waves and sea levels (2017, background): “,(, can be linked to the marginal distributions of X and Y, FX and FY, via a copula C: ( ))(),(),(, yFxFCyxH YXYX = (1) Gudendorf and Segers (2010) provide a sound theoretical justification for using extreme-value copulas in the domain of extreme value multivariate analysis.”
- Copula-Based Measures of Multivariate Association (2010, background): “For a comprehensive discussion regarding extreme value copulas see Gudendorf and Segers [46].”
- Nonparametric estimation of multivariate extreme-value copulas (2011, background): “More generally, they constitute a flexible class of models for describing positive association; see Gudendorf and Segers (2010) for a survey.”
- Large‐sample tests of extreme‐value dependence for multivariate copulas (2011, methodology): “Given that the most frequently used bivariate exchangeable extreme-value copulas such as the Gumbel–Hougaard, Galambos, Hüsler–Reiss and Student extreme-value copulas show striking similarities for a given degree of dependence (see Genest et al., 2011, for a detailed discussion of this matter), onl”
- Characterisation of Drought Properties with Bivariate Copula Analysis (2013, background): “ExtremeValue copula can model very well the dependence structure between rare events as it develop the domain of extreme value theory naturally and it is a suitable selection for general positive dependence structures modeling (Gudendorf and Segers 2010).”