DCCfilter-class {rmgarch}

class: DCC Filter Class

Description

The class is returned by calling the function dccfilter.

Slots

mfilter:

Object of class "vector". Multivariate filter list.

model:

Object of class "vector". Model specification list.

Extends

Class "mGARCHfilter", directly. Class "GARCHfilter", by class "mGARCHfilter", distance 2. Class "rGARCH", by class "mGARCHfilter", distance 3.

Methods

coef

signature(object = "DCCfilter") The coefficient vector (see note).

likelihood

signature(object = "DCCfilter"): The joint likelihood.

rshape

signature(object = "DCCfilter"): The multivariate distribution shape parameter(s).

rskew

signature(object = "DCCfilter"): The multivariate distribution skew parameter(s).

fitted

signature(object = "DCCfilter"): The filtered conditional mean xts object.

sigma

signature(object = "DCCfilter"): The filtered conditional sigma xts object.

residuals

signature(object = "DCCfilter"): The filtered conditional mean residuals xts object.

plot

signature(x = "DCCfilter", y = "missing"): Plot method, given additional arguments ‘series’ and ‘which’.

infocriteria

signature(object = "DCCfilter"): Information criteria.

rcor

signature(object = "DCCfilter"): The filtered dynamic conditional correlation array given additional argument ‘type’ (either “R” for the correlation else will return the “Q” matrix). The third dimension label of the array gives the time index (from which it is then possible to construct pairwise xts objects for example). A further argument ‘output’ allows to switch between “array” and “matrix” returned object.

rcov

signature(object = "DCCfilter"): The filtered dynamic conditional covariance array. The third dimension label of the array gives the time index (from which it is then possible to construct pairwise xts objects for example). A further argument ‘output’ allows to switch between “array” and “matrix” returned object.

show

signature(object = "DCCfilter"): Summary.

nisurface

signature(object = "DCCfilter"): The news impact surface plot given additional arguments ‘type’ with either “cov” or “cor” (for the covariance and correlation news impact respectively), ‘pair’ denoting the asset pair (defaults to c(1,2)), ‘plot’ (logical) and ‘plot.type’ with a choice of either “surface” or “contour”.

Note

The ‘coef’ method takes additional argument ‘type’ with valid values ‘garch’ for the univariate garch parameters, ‘dcc’ for the second stage dcc parameters and by default returns all the parameters in a named vector.

Author(s)

Alexios Galanos

References

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