Method for creating a DCC-GARCH fit object.

```
dccfit(spec, data, out.sample = 0, solver = "solnp", solver.control = list(),
fit.control = list(eval.se = TRUE, stationarity = TRUE, scale = FALSE),
cluster = NULL, fit = NULL, VAR.fit = NULL, realizedVol = NULL, ...)
```

spec

A `'>DCCspec`

object created by calling
`dccspec`

.

data

A multivariate data object of class xts or one which can be coerced to such.

out.sample

A positive integer indicating the number of periods before the last to keep for out of sample forecasting.

solver

Either “nlminb”, “solnp”, “gosolnp” or “lbfgs”. It can also optionally be a vector of length 2 with the first solver being used for the first stage univariate GARCH estimation (in which case the option of “hybrid” is also available).

solver.control

Control arguments list passed to optimizer.

fit.control

Control arguments passed to the fitting routine. The ‘eval.se’ option determines whether standard errors are calculated (see details below). The ‘stationarity’ option is for the univariate stage GARCH fitting routine, whilst for the second stage DCC this is imposed by design. The ‘scale’ option is also for the first stage univariate GARCH fitting routine.

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 (remember to stop the cluster on completion).

VAR.fit

(optional) A previously estimated VAR object returned from
calling the `varxfit`

function.

realizedVol

Required xts matrix for the realGARCH model.

...

.

The 2-step DCC estimation fits a GARCH-Normal model to the univariate data and
then proceeds to estimate the second step based on the chosen multivariate
distribution. Because of this 2-step approach, standard errors are expensive to
calculate and therefore the use of parallel functionality, built into both the
fitting and standard error calculation routines is key. The switch to turn off
the calculation of standard errors through the ‘fit.control’ option could
be quite useful in rolling estimation such as in the `dccroll`

routine.
The optional ‘fit’ argument allows to pass your own `'>uGARCHmultifit`

object instead of having the routine estimate it. This is very useful in cases
of multiple use of the same fit and problems in convergence which might require
a more hands on approach to the univariate fitting stage. However, it is up to
the user to ensure consistency between the ‘fit’ and supplied ‘spec’.