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VineCopula (version 1.0)

VineCopula-package: Statistical inference of vine copulas

Description

This package provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package CDVine are provided.

Arguments

Remark

The package VineCopula is a continuation of the package CDVine by U. Schepsmeier and E. C. Brechmann. It includes all functions implemented in CDVine for the bivariate case (BiCop-functions).

Bivariate copula families

In this package several bivariate copula families are included for bivariate analysis as well as for multivariate analysis using vine copulas. It provides functionality of elliptical (Gaussian and Student-t) as well as Archimedean (Clayton, Gumbel, Frank, Joe, BB1, BB6, BB7 and BB8) copulas to cover a large bandwidth of possible dependence structures. For the Archimedean copula families rotated versions are included to cover negative dependence too. The two parameter BB1, BB6, BB7 and BB8 copulas are however numerically instable for large parameters, in particular, if BB6, BB7 and BB8 copulas are close to the Joe copula which is a boundary case of these three copula families. In general, the user should be careful with extreme parameter choices. The following table shows the parameter ranges of bivariate copula families with parameters par and par2: lll{ Copula family par par2 Gaussian $(-1,1)$ - Student t $(-1,1)$ $(2,\infty)$ (Survival) Clayton $(0,\infty)$ - (Survival) Gumbel $[1,\infty)$ - Frank $R\backslash{0}$ - (Survival) Joe $(1,\infty)$ - Rotated Clayton (90 and 270 degrees) $(-\infty,0)$ - Rotated Gumbel (90 and 270 degrees) $(-\infty,-1]$ - Rotated Joe (90 and 270 degrees) $(-\infty,-1)$ - (Survival) Clayton-Gumbel (BB1) $(0,\infty)$ $[1,\infty)$ (Survival) Joe-Gumbel (BB6) $[1,\infty)$ $[1,\infty)$ (Survival) Joe-Clayton (BB7) $[1,\infty)$ $(0,\infty)$ (Survival) Joe-Frank (BB8) $[1,\infty)$ $(0,1]$ Rotated Clayton-Gumbel (90 and 270 degrees) $(-\infty,0)$ $(-\infty,-1]$ Rotated Joe-Gumbel (90 and 270 degrees) $(-\infty,-1]$ $(-\infty,-1]$ Rotated Joe-Clayton (90 and 270 degrees) $(-\infty,-1]$ $(-\infty,0)$ Rotated Joe-Frank (90 and 270 degrees) $(-\infty,-1]$ $[-1,0)$ }

R-vine copula models

The specification of an R-vine is done in matrix notation, introduced by Dissmann et al. (2011). One matrix contains the R-vine tree structure, one the copula families utilized and two matrices corresponding parameter values. These four matrices are stored in an RVineMatrix object created by the function RVineMatrix. Each matrix is a d x d lower triangular matrix. Since C- and D-vines are special cases, boundary cases, of R-vines one can write each C- or D-vine in R-vine notation. The transformation of notation to or from an R-vine can be done via C2RVine, D2RVine, R2CVine and R2DVine, which provide an interface to the package CDVine. For more details see the documentation of the functions.

Acknowledgment

We acknowledge substantial contributions by our working group at Technische Universitaet Muenchen, in particular by Carlos Almeida and Aleksey Min. In addition, we like to thank Shing (Eric) Fu, Feng Zhu, Guang (Jack) Yang, and Harry Joe for providing their implementation of the method by Knight (1966) for efficiently computing the empirical Kendall's tau. We are especially grateful to Harry Joe for his contributions to the implementation of the bivariate Archimedean copulas.

Details

ll{ Package: VineCopula Type: Package Version: 1.0 Date: 2012-06-28 License: GPL (>=2) Depends: R ($\geq 2.11.0$), MASS, mvtnorm, igraph0 Suggests: CDVine, TSP LazyLoad: yes }

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. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079276. 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 (2011). Selecting and estimating regular vine copulae and application to financial returns. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079277. 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. Chapman and Hall, London. 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. Schepsmeier, U. and J. Stoeber (2012). Derivatives and Fisher information of bivariate copulas. Submitted for publication. http://mediatum.ub.tum.de/node?id=1106541. Stoeber, J. and U. Schepsmeier (2012). Is there significant time-variation in multivariate dependence? In preparation. http://de.arxiv.org/abs/1205.4841.