dccfit-methods {rmgarch}

function: DCC-GARCH Fit

Description

Method for creating a DCC-GARCH fit object.

Usage

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, ...)

Arguments

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).

fit

(optional) A previously estimated univariate uGARCHmultifit object (see details).

VAR.fit

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

realizedVol

Required xts matrix for the realGARCH model.

...

.

Details

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’.

Value

A DCCfit object containing details of the DCC-GARCH fit.

Note

There is no check on the VAR.fit list passed to the method so particular care should be exercised so that the same data used in the fitting routine is also used in the VAR fit routine. This this must have been called with the option postpad ‘constant’. The ability to pass this list of the pre-calculated VAR model is particularly useful when comparing different models (such as copula-GARCH, GO-GARCH etc) using the same dataset and VAR method (i.e. the same first stage conditional mean filtration). Though the classical VAR estimation is very fast and may not require this extra step, the robust method is slow and therefore benefits from calculating this only once.
For extensive examples look in the ‘rmgarch.tests’ folder.

Author(s)

Alexios Galanos