rmgarch (version 1.3-9)

gogarchfit-methods: function: GO-GARCH Filter

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(), cluster = NULL, 
VAR.fit = NULL, ARcoef = 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.

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).

VAR.fit

(optional) A previously estimated VAR list returned from calling the varxfilter function.

ARcoef

An optional named matrix of the fitted AR parameters obtained from calling the arfimafit function on each series and then extracting the coefficients (the normal distribution should be used for the AR estimation). The number of columns should be equal to the number of series, and the rows should include the AR coefficients (common lag for all series), ‘sigma’, and if included the mean (‘mu’). The option to pass the coefficients directly rather than letting the function estimate them may be useful for example when there are convergence problems in the arfima routine and user control of each series estimation is desirable.

...

Additional arguments passed to the ICA functions.

Value

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

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
# }