rugarch (version 1.0-3)

ugarchforecast-methods: function: Univariate GARCH Forecasting

Description

Method for forecasting from a variety of univariate GARCH models.

Usage

ugarchforecast(fitORspec, data = NULL, n.ahead = 10, n.roll = 0, out.sample = 0, 
		external.forecasts = list(mregfor = NULL, vregfor = NULL), ...)

Arguments

fitORspec

Either a univariate GARCH fit object of class uGARCHfit or alternatively a univariate GARCH specification object of class uGARCHspec with valid fixed parameters.

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.

...

.

Value

• 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.

Details

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.

See Also

For filtering ugarchfilter,simulation ugarchsim, rolling forecast and estimation ugarchroll, parameter distribution and uncertainty ugarchdistribution, bootstrap forecast ugarchboot.

Examples

# 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")