dccfilter-methods {rmgarch}

function: DCC-GARCH Filter

Description

Method for creating a DCC-GARCH filter object.

Usage

dccfilter(spec, data, out.sample = 0, filter.control = list(n.old = NULL), 
cluster = NULL, varcoef = NULL, realizedVol = NULL, ...) 

Arguments

spec

A DCCspec object created by calling dccspec with fixed parameters for the coefficients.

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.

filter.control

Control arguments passed to the filtering routine (see note.

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).

varcoef

If a VAR model was chosen, then this is the VAR coefficient matrix which must be supplied. No checks are done on its dimension or correctness so it is up to the user to perform the appropriate checks.

realizedVol

Required xts matrix for the realGARCH model.

...

.

Value

A DCCfilter object containing details of the DCC-GARCH filter.

Note

The ‘n.old’ option in the filter.control argument is key in replicating conditions of the original fit. That is, if you want to filter a dataset consisting of an expanded dataset (versus the original used in fitting), but want to use the same assumptions as the original dataset then the ‘n.old’ argument denoting the original number of data points passed to the dccfit function must be provided. This is then used to ensure that some calculations which make use of the full dataset (unconditional starting values for the garch filtering and the dcc model) only use the first ‘n.old’ points thus replicating the original conditions making filtering appropriate for rolling 1-ahead forecasting.
For extensive examples look in the ‘rmgarch.tests’ folder.

Author(s)

Alexios Galanos