Vine copulas are a flexible class of dependence models consisting of
bivariate building blocks (see e.g., Aas et al., 2009). This package is
primarily made for the statistical analysis of vine copula
models. The package includes tools for parameter estimation, model selection,
simulation, goodness-of-fit tests, and visualization. Tools for estimation,
selection and exploratory data analysis of bivariate copula models are
also provided.
The package VineCopula is a continuation of the
package CDVine by U. Schepsmeier and E. C. Brechmann (see Brechmann
and Schepsmeier (2013)). It includes all functions implemented in CDVine for
the bivariate case (BiCop-functions).
Author(s)
Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann, Benedikt
Graeler, Thomas Nagler, Tobias Erhardt
References
Aas, K., C. Czado, A. Frigessi, and H. Bakken (2009).
Pair-copula constructions of multiple dependence. Insurance: Mathematics and
Economics 44 (2), 182-198.
Bedford, T. and R. M. Cooke (2001). Probability density decomposition for
conditionally dependent random variables modeled by vines. Annals of
Mathematics and Artificial intelligence 32, 245-268.
Bedford, T. and R. M. Cooke (2002). Vines - a new graphical model for
dependent random variables. Annals of Statistics 30, 1031-1068.
Brechmann, E. C., C. Czado, and K. Aas (2012). Truncated regular vines in
high dimensions with applications to financial data. Canadian Journal of
Statistics 40 (1), 68-85.
Brechmann, E. C. and C. Czado (2011). Risk management with high-dimensional
vine copulas: An analysis of the Euro Stoxx 50. Statistics & Risk Modeling,
30 (4), 307-342.
Brechmann, E. C. and U. Schepsmeier (2013). Modeling Dependence with C- and
D-Vine Copulas: The R Package CDVine. Journal of Statistical Software, 52
(3), 1-27. http://www.jstatsoft.org/v52/i03/.
Czado, C., U. Schepsmeier, and A. Min (2012). Maximum likelihood estimation
of mixed C-vines with application to exchange rates. Statistical Modelling,
12(3), 229-255.
Dissmann, J. F., E. C. Brechmann, C. Czado, and D. Kurowicka (2013).
Selecting and estimating regular vine copulae and application to financial
returns. Computational Statistics & Data Analysis, 59 (1), 52-69.
Joe, H. (1996). Families of m-variate distributions with given margins and
m(m-1)/2 bivariate dependence parameters. In L. Rueschendorf, B. Schweizer,
and M. D. Taylor (Eds.), Distributions with fixed marginals and related
topics, pp. 120-141. Hayward: Institute of Mathematical Statistics.
Joe, H. (1997). Multivariate Models and Dependence Concepts. London: Chapman
and Hall.
Knight, W. R. (1966). A computer method for calculating Kendall's tau with
ungrouped data. Journal of the American Statistical Association 61 (314),
436-439.
Kurowicka, D. and R. M. Cooke (2006). Uncertainty Analysis with High
Dimensional Dependence Modelling. Chichester: John Wiley.
Kurowicka, D. and H. Joe (Eds.) (2011). Dependence Modeling: Vine Copula
Handbook. Singapore: World Scientific Publishing Co.
Nelsen, R. (2006). An introduction to copulas. Springer
Schepsmeier, U. (2015) Efficient information based goodness-of-fit tests for
vine copula models with fixed margins. Journal of Multivariate Analysis 138,
34-52.