Demean or not to demean

Hi all

I was hoping someone could shed light or direct me to a resource (or two)
regarding a "demean" question.

As I understand, QMLEs estimated on "demeaned" log return data vs straight
"log return" data behave quite differently in finite samples (particularly
for nonlinear MA models where the MA parameter is of interest). Apparently,
for linear AR models, demeaning data does not seriously affect estimation
of non-intercept parameters (refer: Yong Bao "Should We Demean Data?").

For monthly financial log return data, I find ARMA specifications are not
significant, but some sample *means *ARE significant, while others are not.
In either case, I add the GARCH model specification with various error
distributions from the "rugarch" package.

Code example:
x.log.ret = diff(log(price.x) #i.e. not "demeaned"
spec <- ugarchspec(variance.model=list(model="sGARCH",garchOrder=c(1,1),
             submodel=NULL,external.regressors=NULL,variance.targeting=F),
             mean.model=list(*armaOrder = c(0,0)*,* include.mean = T*,
external.regressors=NULL),
             distribution.model="std")
tempgarch <- ugarchfit(spec=spec,data=x.log.ret,solver="hybrid")

I work through the steps necessary to fitting a *t*Copula from which to
simulate and ultimately work my way back to simulated returns.

The goal here is to extract from the matrix of simulated returns those
groups of returns coinciding with certain pre-determined "scenarios". These
are then used for portfolio optimization.

In the "global respect" of the methodology, can anyone shed light on the
merits/demerits of not first demeaning the data? I haven't found any
glaring problems, but it bothers me that the "rugarch" package operates on
demeaned data.

Thank you very much for the help
Gareth