Results 71 to 80 of about 66,819 (259)
Stress Testing German Industry Sectors: Results from a Vine Copula Based Quantile Regression
, 2017 Measuring interdependence between probabilities of default (PDs) in different industry sectors of an economy plays a crucial role in financial stress testing. Thereby, regression approaches may be employed to model the impact of stressed industry sectors
Czado, Claudia +3 more
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Examination and visualisation of the simplifying assumption for vine copulas in three dimensions [PDF]
, 2016 Vine copulas are a highly flexible class of dependence models, which are based on the decomposition of the density into bivariate building blocks. For applications one usually makes the simplifying assumption that copulas of conditional distributions are
Matthias Killiches +2 more
semanticscholar +1 more source
A Note on Identification of Bivariate Copulas for Discrete Count Data
, 2017 Copulas have enjoyed increased usage in many areas of econometrics, including applications with discrete outcomes. However, Genest and Neslehova (2007) present evidence that copulas for discrete outcomes are not identified, particularly when those ...
P. Trivedi, David M. Zimmer
semanticscholar +1 more source
Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine
Journal of Statistical Software, 2013 Flexible multivariate distributions are needed in many areas. The popular multivariate Gaussian distribution is however very restrictive and cannot account for features like asymmetry and heavy tails.
Eike Christian Brechmann +1 more
doaj
Pair-Copula Constructions of Multivariate Copulas
, 2010 In this survey we introduce and discuss the pair-copula construction method to build flexible multivariate distributions. This class includes drawable (D), canonical (C) and regular vines developed in [5] and [4]. Estimation and model selection methods are studied both in a classical as well as in a Bayesian setting. This flexible class of multivariate
openaire +2 more sources
A compendium of expressions for dependence measures of bivariate copulas
AIMS MathematicsCopulas are important because they allow for modeling and analyzing the dependence structure between random variables, providing insights into complex relationships beyond linear correlations.
Saralees Nadarajah, Victor Nawa
doaj +1 more source
Dependence Modeling, 2018 We review various methods for constructing bivariate copulas with given diagonal sections from seminal work to the most recent research on copulas with given diagonal and opposite diagonal sections.
Fernández-Sánchez Juan +1 more
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Some results on weak and strong tail dependence coefficients for means of copulas [PDF]
Copulas represent the dependence structure of multivariate distributions in a natural way. In order to generate new copulas from given ones, several proposals found its way into statistical literature.
Fischer, Matthias J., Klein, Ingo
core
New Bivariate Copulas via Lomax Distribution Generated Distortions
AppliedMathWe develop a framework for creating distortion functions that are used to construct new bivariate copulas. It is achieved by transforming non-negative random variables with Lomax-related distributions.
Fadal Abdullah Ali Aldhufairi +1 more
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Bayesian Model Selection of Regular Vine Copulas
Bayesian Analysis, 2017 Regular vine copulas can describe a wider array of dependency patterns than the multivariate Gaussian copula or the multivariate Student’s t copula.
Lutz F. Gruber, C. Czado
semanticscholar +1 more source