Description Usage Arguments Details Value Note Author(s)
Method for creating a CopulaGARCH fit object.
1 2 3 4  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,...)

spec 
A 
data 
A multivariate xts data object or one which can be coerced to such. 
out.sample 
A positive integer indicating the number of periods before the last to keep for out of sample forecasting. 
solver 
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). 
solver.control 
Control arguments list passed to optimizer. 
fit.control 
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. 
cluster 
A cluster object created by calling 
fit 
(optional) A previously estimated univariate

VAR.fit 
(optional) A previously estimated VAR list returned from
calling the 
spd.control 
If the spd transformation was chosen in the
specification, the spd.control passes its arguments to the

realizedVol 
Required xts matrix for the realGARCH model. 
... 
. 
The CopulaGARCH models implemented can either be timevarying 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, semiparametric, and empirical). For the semiparametric 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’).
A cGARCHfit
Object containing details of the CopulaGARCH
fit.
There is no check on the VAR.fit list passed to the method so particular care
should be exercised so that the same data used in the fitting routine is also
used in the VAR fit routine. This must have been called with the option
postpad
‘constant’. The ability to pass this list of the
precalculated VAR model is particularly useful when comparing different models
(such as DCC GARCH, GO GARCH etc) using the same dataset and VAR method (i.e.
the same first stage conditional mean filtration). Though the classical VAR
estimation is very fast and may not require this extra step, the robust method
is slow and therefore benefits from calculating this only once.
For extensive examples look in the ‘rmgarch.tests’ folder.
Alexios Galanos
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