Multivariate Generalized Pareto Distributions

By Holger Rootz'en, N. Tajvidi. 2006

214 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/648eec09e517b08fa255f1c1470c32d63d0ea965

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Thomas Opitz
2. Raphaël Huser
3. A. C. Davison
4. Emeric Thibaud
5. Mathieu Ribatet
6. Jennifer L. Wadsworth
7. Clément Dombry
8. Ana Ferreira

In Finance

1. Thomas Opitz
2. Raphaël Huser
3. A. C. Davison
4. Emeric Thibaud
5. Mathieu Ribatet
6. Sebastian Engelke
7. Jennifer L. Wadsworth
8. Clément Dombry

Citations in the corpus

• Multivariate multiparameter extreme value models and return periods: A copula approach (2010, background): “Only recently, new multivariate extreme value models were introduced to overcome some of these limitations (see below and also Tawn [1990], Coles and Tawn [1991], Joe [1994], and Rootzen and Tajvidi [2006] ).”
• Extremal dependence of random scale constructions (2018, background): “By understanding which facets of the construction lead to different dependence properties, we are able to determine dependence models that can capture both types of extremal dependence within a single parametric family; the recent proposals in Wadsworth et al. (2017), Huser et al.”
• The generalized Pareto process; with a view towards application and simulation (2012, methodology): “The main difference from Rootz´en and Tajvidi (2006) is that their analysis is entirely based on distribution functions whereas ours is more structural.”
• Space–time modelling of extreme events (2012, methodology): “Alternative approaches recently proposed are based on generalized Pareto processes (Ferreira and de Haan, 2012; Aulbach and Falk, 2012) and multivariate generalized Pareto distributions (Rootzén and Tajvidi (2006), Buishand et al. (2008) and Beirlant et al. (2004), page 277).”
• Functional regular variations, Pareto processes and peaks over threshold (2015, methodology): “Extensions to the multivariate case have been proposed by Rootzen and Tajvidi [19] and Falk et al. [11].”
• Extremes on river networks (2015, background): “One might call a multivariate observation extreme if at least one component is large, leading to multivariate generalized Pareto distributions [Rootzén and Tajvidi (2006)], whereas choosing data where a single fixed component exceeds a high threshold gives a conditional extreme value model…”
• Multivariate peaks over thresholds models (2016, unknown): “, d} has the following characterizations (Rootzén and Tajvidi, 2006)1.”
• Efficient inference and simulation for elliptical Pareto processes (2013, background): “In univariate and multivariate theory, a generalized Pareto limit is obtained by conditioning on the exceedance of a high threshold in at least one component (Beirlant et al., 2004; Rootzén & Tajvidi, 2006).”
• Univariate and bivariate GPD methods for predicting extreme wind storm losses (2009, methodology): “A main issue for PoT estimation is how to handle observations where one component is extreme and the other not, cf. Rootzén and Tajvidi (2006).”