cgarchfit-methods: function: Copula-GARCH Fit
In rmgarch: Multivariate GARCH models

Description Usage Arguments Details Value Note Author(s)

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,...)

`spec`	A `cGARCHspec` A `cGARCHspec` object created by calling `cgarchspec`.
`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 `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).
`fit`	(optional) A previously estimated univariate `uGARCHmultifit` object (see details).
`VAR.fit`	(optional) A previously estimated VAR list returned from calling the `varxfit` function.
`spd.control`	If the spd transformation was chosen in the specification, the spd.control passes its arguments to the `spdfit` routine of the `spd` package.
`realizedVol`	Required xts matrix for the realGARCH model.
`...`	.

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’).

A cGARCHfit Object containing details of the Copula-GARCH 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 pre-calculated 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 Ghalanos

rmgarch documentation built on May 2, 2019, 5:56 p.m.