Description Slots Extends Methods Author(s) References

The class is returned by calling the function `dccforecast`

.

`mforecast`

:Object of class

`"vector"`

Multivariate forecast list.`model`

:Object of class

`"vector"`

Model specification list.

Class `"mGARCHforecast"`

, directly.
Class `"GARCHforecast"`

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

, by class "mGARCHforecast", distance 3.

- rshape
`signature(object = "DCCforecast")`

: The multivariate distribution shape parameter(s).- rskew
`signature(object = "DCCforecast")`

: The multivariate distribution skew parameter(s).- fitted
`signature(object = "DCCforecast")`

: The conditional mean forecast array of dimensions n.ahead x n.assets by (n.roll+1). The thirds dimension of the array has the T+0 index label.- sigma
`signature(object = "DCCforecast")`

: The conditional sigma forecast array of dimensions n.ahead x n.assets by (n.roll+1). The thirds dimension of the array has the T+0 index label.- plot
`signature(x = "DCCforecast", y = "missing")`

: Plot method, given additional arguments ‘series’ and ‘which’.- rcor
`signature(object = "DCCforecast")`

: The forecast dynamic conditional correlation list of arrays of length (n.roll+1), with each array of dimensions n.assets x n.assets x n.ahead. The method takes on one additional argument ‘type’ (either “R” for the correlation else will return the DCC Q matrix).- rcov
`signature(object = "DCCforecast")`

: The forecast dynamic conditional correlation list of arrays of length (n.roll+1), with each array of dimensions n.assets x n.assets x n.ahead.- show
`signature(object = "DCCforecast")`

: Summary.

Alexios Ghalanos

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

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