Results 151 to 160 of about 276 (185)
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Vine copula regression for observational studies
AStA Advances in Statistical Analysis, 2019zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Roger M. Cooke, Harry Joe, Bo Chang
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Comparing Regular Vine Copula Models
2019In this chapter, we want to compare the fit of two or more regular vine copula specifications for a given copula data set.
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Vine Copula Specifications for Stationary Multivariate Markov Chains
Journal of Time Series Analysis, 2014Vine copulae provide a graphical framework in which multiple bivariate copulae may be combined in a consistent fashion to yield a more complex multivariate copula. In this article, we discuss the use of vine copulae to build flexible semiparametric models for stationary multivariate higher‐order Markov chains.
Beare, Brendan K., Seo, Juwon
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Vine-copula GARCH model with dynamic conditional dependence
Computational Statistics & Data Analysis, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
So, Mike Ka Pui, Yeung, Cherry Y.T.
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Multi-model D-vine copula regression model with vine copula-based dependence description
Computers & Chemical Engineering, 2022Shisong Liu, Shaojun Li
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2010
Empirical researches in financial literature have shown evidence of a skewness and a time conditioning in the univariate behaviour of stock returns and, overall, in their dependence structure. The inadequacy of the elliptical and, in general, symmetrical multivariate constant model assumptions, when this type of dependence occurs, is an almost stylized
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Empirical researches in financial literature have shown evidence of a skewness and a time conditioning in the univariate behaviour of stock returns and, overall, in their dependence structure. The inadequacy of the elliptical and, in general, symmetrical multivariate constant model assumptions, when this type of dependence occurs, is an almost stylized
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2017
Copulas and vines allow us to model the distribution of multivariate random variables in a flexible way. This article introduces copulas via Sklar's theorem, explains how pair copula constructions are built by decomposing multivariate copula densities, and illustrates vine graphical representations.
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Copulas and vines allow us to model the distribution of multivariate random variables in a flexible way. This article introduces copulas via Sklar's theorem, explains how pair copula constructions are built by decomposing multivariate copula densities, and illustrates vine graphical representations.
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Simulating Regular Vine Copulas and Distributions
2019For simulation from a d-dimensional distribution function \(F_{1,..., d}\) with conditional distribution functions \(F_{j|1,\ldots , j-1}(\cdot |x_1,\ldots , x_{j-1})\) and their inverses \(F_{j|1,\dots , j-1}^{-1}(\cdot |x_1,\ldots , x_{j-1})\) for \(j=2,\ldots , d\) we can use iterative inverse probability transformations.
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Vine copula Granger causality in mean
Economic Modelling, 2022Hyuna Jang, Jong-Min Kim, Hohsuk Noh
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Selection of Regular Vine Copula Models
2019The full specification of a vine copula model requires the choice of a vine tree structure, copula families for each pair copula term and their corresponding parameters. In this chapter, we discuss different frequentist selection and estimation approaches.
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