cgarchsim-methods: function: Copula-GARCH Simulation
In rgarch: Flexible GARCH modelling in R

Description Usage Arguments Details Value Author(s) References Examples

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, rseed = NULL, 
mexsimdata = NULL, vexsimdata = NULL, parallel = FALSE, parallel.control = list(pkg = c("multicore", 
"snowfall"), cores = 2), VAR.fit = NULL, ...)

`fit`	A `cGARCHfit` object created by calling either `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.
`presigma`	Allows the starting sigma values to be provided by the user.
`prereturns`	Allows the starting return data to be provided by the user.
`preresiduals`	Allows the starting residuals to be provided by the user.
`preR`	Allows the starting correlation to be provided by the user.
`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.
`parallel`	Whether to make use of parallel processing on multicore systems.
`parallel.control`	The parallel control options including the type of package for performing the parallel calculations (‘multicore’ for non-windows O/S and ‘snowfall’ for all O/S), and the number of cores to make use of.
`VAR.fit`	An optional VAR.fit list returned from calling the `varxfilter` function. This is not currently used but is for future expandability of the method to dispatch via a `cGARCHspec` object.
`...`	.

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.

A cGARCHsim object containing details of the Copula-GARCH simulation.

Alexios Ghalanos

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.

## Not run: 
data(dji30ret)
Dat = dji30ret[, 1:3, drop = FALSE]
# Copula-Student (non time varying - Max Likelihood Estimation of Rho)
uspec = ugarchspec(mean.model = list(armaOrder = c(1,0)), variance.model = list(model = "sGARCH"), 
		distribution.model = "norm")
spec = cgarchspec(uspec = multispec( replicate(3, uspec) ), VAR = TRUE, VAR.opt = list(lag = 1, lag.max = 4, 
lag.criterion = c("AIC", "HQ", "SC", "FPE"), external.regressors = NULL), 
dccOrder = c(1,1), distribution.model = list(copula = c("mvnorm"), method = c("ML"),
time.varying = TRUE, transformation = "parametric"), start.pars = list(), fixed.pars = list())	

fit1 = cgarchfit(spec, data = Dat, spd.control = list(lower = 0.1, upper = 0.9, type = "pwm", kernel = "epanech"), 
		fit.control = list(eval.se = FALSE, trace = TRUE), solver = "solnp")
preR = last(rcor(fit1),1)[,,1]
diag(preR) = 1
# a '.fitlist' (univariate uGARCHmultifit) is always returns to the global environment when fitting 
# mgarch models.
sim1 = cgarchsim(fit1, n.sim = 1, n.start = 0, m.sim = 3500, presigma = tail(sigma(fit1), 1), 
startMethod = "sample", preR = preR, prereturns = tail( as.matrix(Dat), 4), 
preresiduals = tail(residuals(.fitlist), 1),rseed = 1:3500)

simmean1 = c(mean(sapply(sim1@msim$simX, FUN = function(x) x[,1])),
		mean(sapply(sim1@msim$simX, FUN = function(x) x[,2])),
		mean(sapply(sim1@msim$simX, FUN = function(x) x[,3])))

forcmean = round( rgarch:::varxforecast(X = Dat, Bcoef = fit1@mfit$vrmodel$Bcoef, p = 4, 
				out.sample = 0, n.ahead = 1, n.roll = 0, mregfor = NULL), 5)
tmp = data.frame(sim = round(simmean1,5), forc = round(as.numeric(forcmean),5), row.names = colnames(Dat))
print(tmp)	

## End(Not run)