gogarchfit-methods: function: GO-GARCH Filter

In rgarch: Flexible GARCH modelling in R

Description

Method for filtering the GO-GARCH model.

Usage

gogarchfit(spec, data, out.sample = 0, solver = "solnp", fit.control = list(stationarity = 1), 
solver.control = list(), parallel = FALSE, parallel.control = list(pkg = c("multicore", "snowfall"), cores = 2), 
VAR.fit = NULL, ...) 

Arguments

spec	A GO-GARCH spec object of class goGARCHspec.
data	A multivariate data object. Can be a matrix or data.frame or timeSeries.
out.sample	A positive integer indicating the number of periods before the last to keep for out of sample forecasting.
solver	One of either “nlminb”, “solnp” or “gosolnp”.
solver.control	Control arguments list passed to optimizer.
fit.control	Control arguments passed to the fitting routine. Stationarity explicitly imposes the variance stationarity constraint during optimization.
parallel	Whether to make use of parallel processing on multicore systems.
parallel.control	The parallel control options including the type of package for performing the parallel calculations (‘multicore’ for non-windows O/S and ‘snowfall’ for all O/S), and the number of cores to make use of.
VAR.fit	(optional) A previously estimated VAR list returned from calling the varxfilter function.
...	Additional argument passed to the ICA function.

Value

A goGARCHfit object containing details of the GO-GARCH fit.

Note

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 filter routine. The ability to pass this list of the pre-calculated VAR model is particularly useful when comparing different models (such as copula GARCH, DCC GARCH etc) using the same dataset and VAR method. 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.

Author(s)

Alexios Ghalanos

Examples

## Not run: 
data(dji30ret)
spec = gogarchspec(mean.model = list(demean = "constant"), 
		variance.model = list(model = "sGARCH", garchOrder = c(1,1), submodel = NULL), 
		distribution.model = list(distribution = "manig"),
		ica = "fastica")

fit = gogarchfit(spec = spec, data  = dji30ret[,1:4, drop = FALSE],  out.sample = 50, gfun = "tanh")
fit

## End(Not run)

rgarch documentation built on May 2, 2019, 5:22 p.m.