Thank you Alexios for quick reply!! The files in the 'rmgarch.tests' folder
gave me a lot of help.
Can I do 1-step ahead forecasting using GARCH-Copula with the help of
rmgarch package? for example I need to get the returns of each assets and
their covariance at T+1 based on the parameters estimated using in-sample
data until T?
I followed your Example in the help of cgarchsim. Please correct me if I am
wrong:
1) the (mean) forecast returns should be the simmean1 in your example
2) I think rcov(sim1) only report the 1st cov out of 3500 simulations. Then
how to get the mean forecast cov?
I think the Example in ?cgarchsim has a copula of errors, although none of
them is serious:
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())
VAR.opt = list(lag = 1, lag.max = 4, lag.criterion = c("AIC", "HQ", "SC",
"FPE"), external.regressors = NULL) need to be lag = 1, lag.max = 4,
lag.criterion = c("AIC", "HQ", "SC", "FPE"), external.regressors = NULL
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)
if I use prereturns = tail( as.matrix(Dat), 4) , it will report error. So
instead, I use prereturns = tail( as.matrix(Dat), 1)
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)
the rgarch package is offline. So can you suggest another way for this?
Thank you again!
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