dccforecast-methods: function: DCC-GARCH Forecast

Description Usage Arguments Details Value Author(s) References


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).




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.


A DCCforecast object containing details of the DCC-GARCH forecast.


Alexios Galanos


Engle, R.F. and Sheppard, K. 2001, Theoretical and empirical properties of dynamic conditional correlation multivariate GARCH, NBER Working Paper.

rmgarch documentation built on Feb. 5, 2022, 1:07 a.m.