The class is returned by calling the function `dccfilter`

.

`mfilter`

:Object of class

`"vector"`

. Multivariate filter list.`model`

:Object of class

`"vector"`

. Model specification list.

Class `"mGARCHfilter"`

, directly.
Class `"GARCHfilter"`

, by class "mGARCHfilter", distance 2.
Class `"rGARCH"`

, by class "mGARCHfilter", distance 3.

- 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).- 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).- 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”.

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.

Alexios Ghalanos

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

Questions? Problems? Suggestions? Tweet to @rdrrHQ or email at ian@mutexlabs.com.

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