A question about function: dccsim in rmgarch package
Hi ZHU Cai, The returned object is of class "DCCsim" and more generally "mGARCHsim". All mGARCHsim classes have an "msim" and "model" slot: slotNames(dccsim) [1] "msim" "model" names(dccsim at msim) [1] "simH" "simR" "simQ" "simX" "simZ" "simRes" "rseed" - simH (conditional covariance) is a list of 3d arrays of length m.sim and array length no.assets x no.assets x n.sim - simR (conditional correlation) is a list of 3d arrays of length m.sim and array length no.assets x no.assets x n.sim - simQ (conditional correlation DCC proxy process) is a list of 3d arrays of length m.sim and array length no.assets x no.assets x n.sim - simX (conditional mean) is a list of matrices of length m.sim and dimensions n.sim x no.assets. Therefore, to obtain the m.sim x no.assets matrix of the simulated 1-ahead conditional mean: t(sapply(dccsim at msim$simX, FUN = function(x) x)) Regards, Alexios
On 03/09/2012 09:33, cai zhu wrote:
Dear All:
Recently, I need to do a asset allocation exercise in R using rmgarch
package. One-step ahead out of sample returns is needed. I use the
following code:
uspec <- ugarchspec(mean.model=list(armaOrder=c(1,0), include.mean=FALSE),
variance.model=list(garchOrder=c(1,1), model="sGARCH"),
distribution.model="norm")
mspec <- multispec(replicate(2, uspec))
dspec <- dccspec(mspec, VAR=FALSE, robust=FALSE, lag=1,
dccOrder=c(1, 1), asymmetric=FALSE, type="2-step",
distribution = "mvnorm")
dccgarch <- dccfit(dspec, subdata, solver="solnp",
solver.control=list(trace=TRUE))
dccsim <- dccsim(dccgarch, n.sim=1, m.sim=10000, startMethod="sample")
Basically, I need 10000 different 1-step ahead returns for a 2-dimension
time series. However, I do not know how to extract simulated returns from
dccsim. I have read the rmgarch.tests, but still get no clue about it. Any
help is appreciated. Thank you very much.
Regards
ZHU Cai
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