Method for creating a DCC-GARCH forecast object.
dccforecast(fit, n.ahead = 1, n.roll = 0,
external.forecasts = list(mregfor = NULL, vregfor = NULL), cluster = NULL, ...)A '>DCCfit object created by calling
dccfit.
The forecast horizon.
The no. of rolling forecasts to create beyond the first one (see details).
A list with forecasts for the external regressors in the mean and/or variance equations if specified (see details).
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).
.
A '>DCCforecast object containing details of the DCC-GARCH
forecast.
When using n.roll, it is assumed that dccfit was called
with argument ‘out.sample’ being large enough to cover n-rolling
forecasts.
When n.roll = 0, all forecasts are based on an unconditional n-ahead forecast
routine based on the approximation method described in ENGLE and SHEPPARD (2001)
paper (see reference below). If any external regressors are present, then the
user must pass in their unconditional forecasts in the ‘external.forecasts’
list, as matrices with dimensions equal to n.ahead x n.assets. This assumes
that the univariate GARCH specifications share common external regressors
(this may change in the future).
When n.roll>0 and n.ahead = 1, then this is a pure rolling forecast based on the
available out.sample data provided for in the call to the fit routine. It is
also assumed that if any external regressors were passed to the fit routine that
they contained enough values to cover the out.sample period so that they could
be used in this forecast scenario.
The case of n.roll > 0 AND n.ahead > 1 is not implemented.
Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper.