Method for creating a Copula-GARCH simulation object.

```
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, ...)
```

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 the univariate GARCH dynamics.

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-ahead forecast type simulation (see details below).

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 to obtaining a rolling n-ahead forecast type
simulation (see details below).

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 assumed to be zero.

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 be assumed to be zero.

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.

...

.

A `'>cGARCHsim`

object containing details of the Copula-GARCH
simulation.

Since there is no explicit forecasting routine, the user should use this method for incrementally building up n-ahead forecasts by simulating 1-ahead, obtaining the means of the returns, sigma, Rho etc and feeding them to the next round of simulation as starting values. The ‘rmgarch.tests’ folder contains specific examples which illustrate this particular point.

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.