Description Slots Extends Methods Author(s) References
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.
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.
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.
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.
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.
signature(object = "cGARCHsim")
:
Summary.
Alexios Galanos
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.
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