RVineCopSelect: Sequential Pair-Copula Selection and Estimation for R-Vine...

View source: R/RVineCopSelect.R

RVineCopSelectR Documentation

Sequential Pair-Copula Selection and Estimation for R-Vine Copula Models


This function fits a R-vine copula model to a d-dimensional copula data set. Pair-copula families are selected using BiCopSelect() and estimated sequentially.


  familyset = NA,
  selectioncrit = "AIC",
  indeptest = FALSE,
  level = 0.05,
  trunclevel = NA,
  weights = NA,
  rotations = TRUE,
  se = FALSE,
  presel = TRUE,
  method = "mle",
  cores = 1



N x d data matrix (with uniform margins).


integer vector of pair-copula families to select from. The vector has to include at least one pair-copula family that allows for positive and one that allows for negative dependence. Not listed copula families might be included to better handle limit cases. If familyset = NA (default), selection among all possible families is performed. If a vector of negative numbers is provided, selection among all but abs(familyset) is performed. Coding of pair copula families is the same as in BiCop().


lower or upper triangular d x d matrix that defines the R-vine tree structure.


Character indicating the criterion for pair-copula selection. Possible choices: selectioncrit = "AIC" (default), "BIC", or "logLik" (see BiCopSelect()).


Logical; whether a hypothesis test for the independence of u1 and u2 is performed before bivariate copula selection (default: indeptest = FALSE; see BiCopIndTest()). The independence copula is chosen for a (conditional) pair if the null hypothesis of independence cannot be rejected.


numeric; significance level of the independence test (default: level = 0.05).


integer; level of truncation.


Numerical; weights for each observation (optional).


logical; if TRUE, all rotations of the families in familyset are included.


Logical; whether standard errors are estimated (default: se = FALSE).


Logical; whether to exclude families before fitting based on symmetry properties of the data. Makes the selection about 30\ (on average), but may yield slightly worse results in few special cases.


indicates the estimation method: either maximum likelihood estimation (method = "mle"; default) or inversion of Kendall's tau (method = "itau"). For method = "itau" only one parameter families and the Student t copula can be used (⁠family = 1,2,3,4,5,6,13,14,16,23,24,26,33,34⁠ or 36). For the t-copula, par2 is found by a crude profile likelihood optimization over the interval (2, 10].


integer; if cores > 1, estimation will be parallelized within each tree (using foreach::foreach()). Note that parallelization causes substantial overhead and may be slower than single-threaded computation when dimension, sample size, or family set are small or method = "itau".


R-vine copula models with unknown structure can be specified using RVineStructureSelect().


An RVineMatrix() object with the selected families (RVM$family) as well as sequentially estimated parameters stored in RVM$par and RVM$par2. The object is augmented by the following information about the fit:

se, se2

standard errors for the parameter estimates (if se = TRUE; note that these are only approximate since they do not account for the sequential nature of the estimation,


number of observations,

logLik, pair.logLik

log likelihood (overall and pairwise)

AIC, pair.AIC

Aikaike's Informaton Criterion (overall and pairwise),

BIC, pair.BIC

Bayesian's Informaton Criterion (overall and pairwise),


matrix of empirical values of Kendall's tau,


matrix of p-values of the independence test.



For a comprehensive summary of the vine copula model, use summary(object); to see all its contents, use str(object).


Eike Brechmann, Thomas Nagler


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.

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.

See Also

RVineMatrix(), BiCop(), BiCopSelect(), plot.RVineMatrix(), contour.RVineMatrix()


# define 5-dimensional R-vine tree structure matrix
Matrix <- c(5, 2, 3, 1, 4,
            0, 2, 3, 4, 1,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 1)
Matrix <- matrix(Matrix, 5, 5)
# define R-vine pair-copula family matrix
family <- c(0, 1, 3, 4, 4,
            0, 0, 3, 4, 1,
            0, 0, 0, 4, 1,
            0, 0, 0, 0, 3,
            0, 0, 0, 0, 0)
family <- matrix(family, 5, 5)
# define R-vine pair-copula parameter matrix
par <- c(0, 0.2, 0.9, 1.5, 3.9,
         0, 0, 1.1, 1.6, 0.9,
         0, 0, 0, 1.9, 0.5,
         0, 0, 0, 0, 4.8,
         0, 0, 0, 0, 0)
par <- matrix(par, 5, 5)
# define second R-vine pair-copula parameter matrix
par2 <- matrix(0, 5, 5)

## define RVineMatrix object
RVM <- RVineMatrix(Matrix = Matrix, family = family,
                   par = par, par2 = par2,
                   names = c("V1", "V2", "V3", "V4", "V5"))

## simulate a sample of size 500 from the R-vine copula model
simdata <- RVineSim(500, RVM)

## determine the pair-copula families and parameters
RVM1 <- RVineCopSelect(simdata, familyset = c(1, 3, 4, 5 ,6), Matrix)

## see the object's content or a summary

## inspect the fitted model using plots
## Not run: plot(RVM1)  # tree structure

contour(RVM1)  # contour plots of all pair-copulas

VineCopula documentation built on July 26, 2023, 5:23 p.m.