copula: Multivariate Dependence with Copulas

Marius Hofert · Ivan Kojadinovic A · Martin Maechler · Jun Yan

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Financial Risk and Volatility Modeling Statistical Distribution Estimation and Applications Data Analysis with R

Abstract

Classes (S4) of commonly used elliptical, Archimedean, extreme-value and other copula families, as well as their rotations, mixtures and asymmetrizations. Nested Archimedean copulas, related tools and special functions. Methods for density, distribution, random number generation, bivariate dependence measures, Rosenblatt transform, Kendall distribution function, perspective and contour plots. Fitting of copula models with potentially partly fixed parameters, including standard errors. Serial independence tests, copula specification tests (independence, exchangeability, radial symmetry, extreme-value dependence, goodness-of-fit) and model selection based on cross-validation. Empirical copula, smoothed versions, and non-parametric estimators of the Pickands dependence function.

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References

2010Copula Theory and Its Applications 2007Enjoy the Joy of Copulas: With a Packagecopula 2010Modeling Multivariate Distributions with Continuous Margins Using the copula R Package 2005Non‐parametric Estimation of Tail Dependence 2002Beyond Correlation: Extreme Co-Movements between Financial Assets 2011Nested Archimedean Copulas MeetR: ThenacopulaPackage 2012Likelihood inference for Archimedean copulas in high dimensions under known margins 2006Multivariate conditional versions of Spearman's rho and related measures of tail dependence

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