Usage
cgarchsim(fit, n.sim = 1000, n.start = 0, m.sim = 1,
startMethod = c("unconditional", "sample"), presigma = NULL, preresiduals = NULL,
prereturns = NULL, preR = NULL, preQ = NULL, preZ = NULL, rseed = NULL,
mexsimdata = NULL, vexsimdata = NULL, cluster = NULL, only.density = FALSE,
prerealized = NULL, ...)Arguments
fit
A cGARCHfit object created by calling cgarchfit. n.sim
The simulation horizon.
n.start
The burn-in 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. This is mostly related to th
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.
preR
Allows the starting correlation to be provided by the user and
mostly useful for the static copula.
preQ
Allows the starting DCC-Q value to be provided by the
user and though unnecessary for the first 1-ahead simulation using the
sample option in the startMethod, this is key to obtaining
a rolling n-ahea
preZ
Allows the starting transformed standardized residuals (used in
the DCC model) to be provided by the user and though unnecessary for the
first 1-ahead simulation using the sample option in the
startMethod, this is key
rseed
Optional seeding value(s) for the random number generator.
This should be of length equal to m.sim.
mexsimdata
A list (equal to the number of asset) of matrices of
simulated external regressor-in-mean 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 assum
vexsimdata
A list (equal to the number of asset) of matrices of
simulated external regressor-in-variance 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
cluster
A cluster object created by calling makeCluster from
the parallel package. If it is not NULL, then this will be used for parallel
estimation (remember to stop the cluster on completion).
only.density
Whether to return only the simulated returns (discrete
time approximation to the multivariate density). This is sometimes useful
in order to control memory management for large simulations not requiring
any other information.
prerealized
Allows the starting realized volatility values to be provided
by the user for the univariate GARCH dynamics.