gogarchfit-methods {rmgarch} | R Documentation |
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 |
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 |
VAR.fit |
(optional) A previously estimated VAR list returned from calling the
|
ARcoef |
An optional named matrix of the fitted AR parameters obtained from calling the
|
... |
Additional arguments passed to the ICA functions. |
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 Galanos
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)