Method for creating a DCC-GARCH rolling forecast object.

```
dccroll(spec, data, n.ahead = 1, forecast.length = 50, refit.every = 25,
n.start = NULL, refit.window = c("recursive", "moving"), window.size = NULL,
solver = "solnp", solver.control = list(),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
cluster = NULL, save.fit = FALSE, save.wdir = NULL, realizedVol = NULL,
clusterOnAssets=FALSE, ...)
```

data

A multivariate xts dataset or one which can be coerced to such.

n.ahead

The number of periods to forecast.

forecast.length

The length of the total forecast for which out of sample data from the dataset will be used for testing.

n.start

Instead of forecast.length, this determines the starting point in the dataset from which to initialize the rolling forecast.

refit.every

Determines every how many periods the model is re-estimated.

refit.window

Whether the refit is done on an expanding window including all the previous data or a moving window where all previous data is used for the first estimation and then moved by a length equal to refit.every (unless the window.size option is used instead).

window.size

If not NULL, determines the size of the moving window in the rolling estimation, which also determines the first point used.

solver

The solver to use.

fit.control

Control parameters parameters passed to the fitting function.

solver.control

Control parameters passed to the solver.

cluster

A cluster object created by calling `makeCluster`

from
the parallel package. If it is not NULL, then this will be used for parallel
estimation of the refits (remember to stop the cluster on completion).

save.fit

Whether to save the fitted objects of class
`'>DCCfit`

during the estimation of each (“refit.every”).
If true, the directory to save must be provided. The function will not save
this by default for reasons of memory management, but can save it as an
“.rda” file in the user's chosen directory for further analysis.

save.wdir

realizedVol

Required xts matrix for the realGARCH model.

clusterOnAssets

If a cluster object is provided, use parallel resources on the univariate estimation (TRUE) else on the rolling windows (FALSE).

…

.

A `'>DCCroll`

object containing details of the DCC-GARCH
rolling forecast.