Description Usage Arguments Details Value Author(s)
Method for creating a DCCGARCH simulation object.
1 2 3 4 5  dccsim(fitORspec, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL, preresiduals = NULL,
prereturns = NULL, preQ = NULL, preZ = NULL, Qbar = NULL, Nbar = NULL,
rseed = NULL, mexsimdata = NULL, vexsimdata = NULL, cluster = NULL,
VAR.fit = NULL, prerealized = NULL, ...)

fitORspec 
A 
n.sim 
The simulation horizon. 
n.start 
The burnin sample. 
m.sim 
The number of simulations. 
startMethod 
Starting values for the simulation. Valid methods are “unconditional” for the expected values given the density, and “sample” for the ending values of the actual data from the fit object (for the dispatch method using a specification, “sample” is not relevant). 
presigma 
Allows the starting sigma values to be provided by the user for the univariate GARCH dynamics. 
prereturns 
Allows the starting return data to be provided by the user for the conditional mean simulation. 
preresiduals 
Allows the starting residuals to be provided by the user and used in the GARCH dynamics simulation. 
preQ 
Allows the starting ‘DCCQ’ value to be provided by the
user and though unnecessary for the first 1ahead simulation using the
“sample” option in the 
preZ 
Allows the starting standardized residuals to be provided by
the user and though unnecessary for the first 1ahead simulation using
the “sample” option in the 
Qbar 
The DCC dynamics unconditional Q matrix, required for the specification dispatch method. 
Nbar 
The aDCC dynamics unconditional asymmetry matrix, required for the specification dispatch method. 
rseed 
Optional seeding value(s) for the random number generator. For m.sim>1, it is possible to provide either a single seed to initialize all values, or one seed per separate simulation (i.e. m.sim seeds). However, in the latter case this may result in some slight overhead depending on how large m.sim is. 
mexsimdata 
A list (equal to the number of asset) of matrices of simulated external regressorinmean data with row length equal to n.sim + n.start. If the fit object contains external regressors in the mean equation, this must be provided else will be assumed to be zero. 
vexsimdata 
A list (equal to the number of asset) of matrices of simulated external regressorinvariance data with row length equal to n.sim + n.start. If the fit object contains external regressors in the variance equation, this must be provided else will be assumed to be zero. 
cluster 
A cluster object created by calling 
VAR.fit 
An VAR.fit list returned from calling the

prerealized 
Allows the starting realized volatility values to be provided by the user for the univariate GARCH dynamics. 
... 
. 
In order to pass a correct specification to the filter routine, you must ensure
that it contains the appropriate ‘fixed.pars’ in both the multivariate
DCC part of the specification as well as the multiple univariate specification
part, for which the method setfixed<
should be used.
A DCCsim
object containing details of the DCCGARCH
simulation.
Alexios Ghalanos
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