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Vine-copula GARCH model with dynamic conditional dependence

Computational Statistics & Data Analysis, 2013
zbMATH 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, 2022
Shisong Liu, Shaojun Li
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Bayesian ridge estimators based on a vine copula-based prior in Poisson and negative binomial regression models

Journal of Statistical Computation and Simulation
Poisson and negative binomial regressions are popular methods for modelling the relationship between a count variable and explanatory variables. In the presence of multicollinearity, ridge regression is an alternative to the maximum likelihood estimation
Hirofumi Michimae   +2 more
semanticscholar   +1 more source

TIME VARYING VINE COPULAS

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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Simulating Regular Vine Copulas and Distributions

2019
For 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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Comprehensive multivariate joint distribution model for marine soft soil based on the vine copula

Computers and geotechnics
Xiaohua Bao   +5 more
semanticscholar   +1 more source

Vine copula Granger causality in mean

Economic Modelling, 2022
Hyuna Jang, Jong-Min Kim, Hohsuk Noh
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Selection of Regular Vine Copula Models

2019
The 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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