Modeling Dependence with C- and D-Vine Copulas: The R Package CDVine

By Eike C. Brechmann, Ulf Schepsmeier. 2013

481 citations recorded by the source corpus.

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. Therefore dependence modeling using copulas is nowadays very common to account for such patterns. The use of copulas is however challenging in higher dimensions, where standard multivariate copulas suffer from rather inflexible structures. Vine copulas overcome such limitations and are able to model complex dependency patterns by benefiting from the rich variety of bivariate copulas as building blocks. This article presents the R package CDVine which provides functions and tools for statistical inference of canonical vine (C-vine) and D-vine copulas. It contains tools for bivariate exploratory data analysis and for bivariate copula selection as well as for selection of pair-copula families in a vine. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and graphical methods are also included.

Subfields: Finance, Plant Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/5829bb75de408b5b75fca38d4d35edb71a7342ee

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Citations in the corpus

Sequential Bayesian Model Selection of Regular Vine Copulas (2015, methodology): “The transformations of the copulas’ natural parameters to their Kendall’s τ ’s is provided in Tables 1 and 2 of Brechmann and Schepsmeier (2013).”
Selection of Vine Copulas (2013, methodology): “The frequentist approaches are implemented in the R-packages CDVine [15, 56] for D- and C-vines and VineCopula [57] for regular vines, respectively.”
Nonparametric estimation of simplified vine copula models: comparison of methods (2017, background): “(iv) For all families under consideration there is a one-to-one relationship between the copula parameter and Kendall’s τ (see, e.g., Brechmann and Schepsmeier, 2013, Table 2).”
Total loss estimation using copula-based regression models (2012, background): “An overview on bivariate copula families and their properties, in particular their different tail behavior, can be found e.g. in Brechmann and Schepsmeier (2012).”
Value-at-risk and expected shortfall in cryptocurrencies’ portfolio: a vine copula–based approach (2019, unknown): “…scheme with a window size of 750 days and 875 days in the out-of-sample period8 We used the R packages VineCopula (Schepsmeier et al. 2018), CDVine (Brechmann and Schepsmeier 2013), GAS (Catania, Boudt, and Ardia 2017) and RobGARCHBoot (Trucios 2019) to fit the vine copula, GAS and robust GARCH…”
Probabilistic modeling of flood characterizations with parametric and minimum information pair-copula model (2016, methodology): “An R package called CDvine has been developed which provides the functions and tools used above for statistical inference of canonical vine and D-vine copulas (see Brechmann and Schepsmeier, 2013).”