The class is returned by calling the function `cgarchsim`

.

`msim`

:Object of class

`"vector"`

Multivariate simulation list.`model`

:Object of class

`"vector"`

Model specification list.

Class `"'>mGARCHsim"`

, directly.
Class `"'>GARCHsim"`

, by class "mGARCHsim", distance 2.
Class `"'>rGARCH"`

, by class "mGARCHsim", distance 3.

- fitted
`signature(object = "cGARCHsim")`

: The simulated conditional returns matrix given. Takes optional argument “sim” indicating the simulation run to return (from the m.sim option of the`cgarchsim`

method.- sigma
`signature(object = "cGARCHfit")`

: The simulated conditional sigma matrix given. Takes optional argument “sim” indicating the simulation run to return (from the m.sim option of the`cgarchsim`

method.- rcor
`signature(object = "cGARCHsim")`

: The simulated conditional correlation array (for DCC type). Takes optional argument “sim” indicating the simulation run to return (from the m.sim option of the`cgarchsim`

method. A further argument ‘output’ allows to switch between “array” and “matrix” returned object.- rcov
`signature(object = "cGARCHsim")`

: The simulated conditional covariance array. Takes optional argument “sim” indicating the simulation run to return (from the m.sim option of the`cgarchsim`

method. A further argument ‘output’ allows to switch between “array” and “matrix” returned object.- show
`signature(object = "cGARCHsim")`

: Summary.

Joe, H. *Multivariate Models and Dependence Concepts*, 1997,
Chapman \& Hall, London.
Genest, C., Ghoudi, K. and Rivest, L. *A semiparametric estimation
procedure of dependence parameters in multivariate families of distributions*,
1995, Biometrika, 82, 543-552.