VineCopula-package: Statistical Inference of Vine Copulas
In VineCopula: Statistical Inference of Vine Copulas
VineCopula-package R Documentation
Statistical Inference of Vine Copulas
Description
Provides tools for the statistical analysis of regular vine copula
models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and
Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>.
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.
Details
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 DESCRIPTION file:
This package was not yet installed at build time.
Remark
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)
Maintainer: Thomas Nagler mail@tnagler.com
Authors:
Ulf Schepsmeier ulf.schepsmeier@tum.de
Jakob Stoeber
Eike Christian Brechmann
Benedikt Graeler
Tobias Erhardt tobias.erhardt@tum.de
Other contributors:
Carlos Almeida [contributor]
Aleksey Min [contributor, thesis advisor]
Claudia Czado [contributor, thesis advisor]
Mathias Hofmann [contributor]
Matthias Killiches [contributor]
Harry Joe [contributor]
Thibault Vatter [contributor]
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v052.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.
Eschenburg, P. (2013). Properties of extreme-value copulas Diploma thesis,
Technische Universitaet Muenchen
https://mediatum.ub.tum.de/node?id=1145695
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. and J. Stoeber (2014). Derivatives and Fisher information of
bivariate copulas. Statistical Papers, 55 (2), 525-542.
https://link.springer.com/article/10.1007/s00362-013-0498-x.
Schepsmeier, U. (2013) A goodness-of-fit test for regular vine copula
models. Preprint https://arxiv.org/abs/1306.0818
Schepsmeier, U. (2015) Efficient information based goodness-of-fit tests for
vine copula models with fixed margins. Journal of Multivariate Analysis 138,
34-52.
Stoeber, J. and U. Schepsmeier (2013). Estimating standard errors in regular
vine copula models. Computational Statistics, 28 (6), 2679-2707
https://link.springer.com/article/10.1007/s00180-013-0423-8#.
White, H. (1982) Maximum likelihood estimation of misspecified models,
Econometrica, 50, 1-26.
See Also
Useful links:
VineCopula documentation built on Sept. 11, 2024, 5:26 p.m.
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| VineCopula-package | R Documentation |
Statistical Inference of Vine Copulas
Description
Provides tools for the statistical analysis of regular vine copula models, see Aas et al. (2009) <doi:10.1016/j.insmatheco.2007.02.001> and Dissman et al. (2013) <doi:10.1016/j.csda.2012.08.010>. 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.
Details
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 DESCRIPTION file:
This package was not yet installed at build time.
Remark
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)
Maintainer: Thomas Nagler mail@tnagler.com
Authors:
Ulf Schepsmeier ulf.schepsmeier@tum.de
Jakob Stoeber
Eike Christian Brechmann
Benedikt Graeler
Tobias Erhardt tobias.erhardt@tum.de
Other contributors:
Carlos Almeida [contributor]
Aleksey Min [contributor, thesis advisor]
Claudia Czado [contributor, thesis advisor]
Mathias Hofmann [contributor]
Matthias Killiches [contributor]
Harry Joe [contributor]
Thibault Vatter [contributor]
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v052.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.
Eschenburg, P. (2013). Properties of extreme-value copulas Diploma thesis, Technische Universitaet Muenchen https://mediatum.ub.tum.de/node?id=1145695
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. and J. Stoeber (2014). Derivatives and Fisher information of
bivariate copulas. Statistical Papers, 55 (2), 525-542.
https://link.springer.com/article/10.1007/s00362-013-0498-x.
Schepsmeier, U. (2013) A goodness-of-fit test for regular vine copula models. Preprint https://arxiv.org/abs/1306.0818
Schepsmeier, U. (2015) Efficient information based goodness-of-fit tests for vine copula models with fixed margins. Journal of Multivariate Analysis 138, 34-52.
Stoeber, J. and U. Schepsmeier (2013). Estimating standard errors in regular
vine copula models. Computational Statistics, 28 (6), 2679-2707
https://link.springer.com/article/10.1007/s00180-013-0423-8#.
White, H. (1982) Maximum likelihood estimation of misspecified models, Econometrica, 50, 1-26.
See Also
Useful links:
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