| ugarchforecast-methods {rugarch} | R Documentation |
Method for forecasting from a variety of univariate GARCH models.
ugarchforecast(fitORspec, data = NULL, n.ahead = 10, n.roll = 0, out.sample = 0, external.forecasts = list(mregfor = NULL, vregfor = NULL), ...)
fitORspec |
Either a univariate GARCH fit object of class |
data |
Required if a specification rather than a fit object is supplied. |
n.ahead |
The forecast horizon. |
n.roll |
The no. of rolling forecasts to create beyond the first one (see details). |
out.sample |
Optional. If a specification object is supplied, indicates how many data points to keep for out of sample testing. |
external.forecasts |
A list with forecasts for the external regressors in the mean and/or variance equations if specified. |
... |
. |
The forecast function has two dispatch methods allowing the user to call it with
either a fitted object (in which case the data argument is ignored), or a
specification object (in which case the data is required) with fixed parameters.
The forecast is based on the expected value of the innovations and hence the
density chosen. One step ahead forecasts are based on the value of the previous
data, while n-step ahead (n>1) are based on the unconditional expectation of the
models.
The ability to roll the forecast 1 step at a time is implemented with the
n.roll argument which controls how many times to roll the n.ahead
forecast. The default argument of n.roll = 0 denotes no rolling and returns the
standard n.ahead forecast. Critically, since n.roll depends on data being
available from which to base the rolling forecast, the ugarchfit
function needs to be called with the argument out.sample being at least
as large as the n.roll argument, or in the case of a specification being used
instead of a fit object, the out.sample argument directly in the forecast
function.
A uGARCHforecast object containing details of the GARCH
forecast. See the class for details on the returned object and methods for
accessing it and performing some tests.
Alexios Ghalanos
For filtering ugarchfilter,simulation ugarchsim,
rolling forecast and estimation ugarchroll, parameter distribution
and uncertainty ugarchdistribution, bootstrap forecast
ugarchboot.
## Not run: # Basic GARCH(1,1) Spec data(dmbp) spec = ugarchspec() fit = ugarchfit(data = dmbp[,1], spec = spec) forc = ugarchforecast(fit, n.ahead=20) forc head(as.data.frame(forc)) #plot(forc,which="all") ## End(Not run)