Method for creating a Copula-GARCH fit object.
cgarchfit(spec, data, spd.control = list(lower = 0.1, upper = 0.9, type = "pwm",
kernel = "epanech"), fit.control = list(eval.se = TRUE, stationarity = TRUE,
scale = FALSE), solver = "solnp", solver.control = list(), out.sample = 0,
cluster = NULL, fit = NULL, VAR.fit = NULL, realizedVol = NULL,...)A '>cGARCHspec A '>cGARCHspec
object created by calling cgarchspec.
A multivariate xts data object or one which can be coerced to such.
A positive integer indicating the number of periods before the last to keep for out of sample forecasting.
Either “nlminb”, “solnp”, “gosolnp” or “lbfgs”. It can also optionally be a vector of length 2 with the first solver being used for the first stage univariate GARCH estimation (in which case the option of “hybrid” is also available).
Control arguments list passed to optimizer.
Control arguments passed to the fitting routine. The ‘eval.se’ option determines whether standard errors are calculated (see details below). The ‘scale’ option is for the first stage univariate GARCH fitting routine.
A cluster object created by calling makeCluster from
the parallel package. If it is not NULL, then this will be used for parallel
estimation (remember to stop the cluster on completion).
(optional) A previously estimated VAR list returned from
calling the varxfit function.
If the spd transformation was chosen in the
specification, the spd.control passes its arguments to the
spdfit routine of the spd package.
Required xts matrix for the realGARCH model.
.
A '>cGARCHfit Object containing details of the Copula-GARCH
fit.
The Copula-GARCH models implemented can either be time-varying of DCC variety
else static. The multivariate Normal and Student distributions are used in the
construction of the copulas, and 3 transformation methods are available
(parametric, semi-parametric, and empirical). For the semi-parametric case the
‘spd’ package of the author is available to download from CRAN and fits a
Gaussian kernel in the interior and gpd distribution for the tails (see that
package for more details).
The static copula allows for the estimation of the correlation matrix either by
Maximum Likelihood or the Kendall method for the multivariate Student.
Note that the ‘cgarchfit’ method will assign to the global environment
the '>uGARCHmultifit once that is estimated in order to allow
the routine to be restarted should something go wrong (it should show up as
‘.fitlist’).