Method for forecasting from a variety of univariate GARCH models.

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`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. |

`...` |
For the multiplicative component sGARCH model (mcsGARCH), the
additional argument ‘DailyVar’ is required and should be an xts object of
the daily forecasted variance for the period under consideration to be used
with the intraday data. |

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`

.

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Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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