I've tried Google for this question and am coming up short. Apologies in 
advance if I overlooked something in the archives ...

What I'm wondering is if anyone can point out issues to be aware of in 
using dummy times series that would satisfy specific mean, standard 
deviation and correlation criteria?  What I would like to do is use some 
of the Rmetrics functions for efficient portfolios in asset allocation 
exercise, but these seem to require time series as their inputs and not 
a set of expected returns and covariance matrix.  Rather than rewrite a 
bunch of code, I thought creating dummy data would get me around the 
hurdle, like this :

expRet <- c(0.1, 0.05, 0.08)

stdvns <- diag(c(0.01,0.03,0.05))

corrM<-matrix(c(1.0,0.9,0.6,
                        0.9,1.0,0.7,
                        0.6,0.7,1.0),ncol=3,nrow=3)

covM <- stdvns %*%  corrM %*% stdvns

testdata<-(mvrnorm(n=5000*length(stdvns), mu=expRet, Sigma=covM))

testdata2<-timeSeries(testdata)

I realize that higher moments in the data are not modeled here, but that 
is not info I would typically have in some of these exercises. Is there 
something I am overlooking that anyone can point out?

Regards,
Matt
M<-matrix(c(1.0,0.9,0.6,
                        0.9,1.0,0.7,
                        0.6,0.7,1.0),ncol=3,nrow=3)

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