[R-SIG-Finance] Help with portfolioData/fPortfolio?
matt at considine.net
matt at considine.net
Fri Jan 27 21:29:10 CET 2012
I am trying to run some Rmetrics code (e.g. feasiblePortfolio,
frontierPortfolio) using data that is already set up as expected
returns and a variance/covariance matrix.
The fPortfolio documentation suggests that the "data" argument to
portfolioData
is
"a time series or a named list, containing either a series of returns or named
entries ?mu? and ?Sigma? being mean and covariance matrix."
Yet when I try to run this on code like this (example in mail archives) :
#install.packages("MBESS") #for cor2cov
mu <- c( 0.1, 0.08, 0.065)
sigma <- c( 0.18, 0.12, 0.09 )
correlationMatrix <- rbind( c( 1, 0.8, 0.9 ),
c( 0.8, 1, 0.75),
c( 0.9, 0.75, 1) )
covarianceMatrix <- cor2cov(correlationMatrix, sigma )
data = list( mu = mu, Sigma = covarianceMatrix )
frontier<-portfolioFrontier(data)
I get the message
Error: class(data) == "timeSeries" is not TRUE
Now obviously I know what the error message means. But it suggests
that I am overlooking something such as a parameter or that the doc is
wrong. Further down in the documentation there is a description of
this dataset
Simulated Mean-Cov Data Set:
This data is taken from chapter 1.3.2 in Scherer, M., Martin, R.D.
(2005); Introduction To Modern Portfolio Optimization with NuOPT,
S-PLUS and S+Bayes, Springer, Berlin. It is a list of covariance
matrix and the return means of imaginary assets. It is an example set
for learning about optimization.
which suggests to mean that I am missing something.
Can anyone tell me
a) can I run the Rmetrics codes above using mu and Sigma and if so
what parameters do I need to use or what do I need to do differently?
b) where can I find the "Simulated Mean-Cov" dataset so that I can
play with it and otherwise see if I have misformatted my inputs? Is
there an example available that I can be pointed to which would show
how it is used (and which would presumably answer question (a)?
Thank you in advance for any advice, pointers, code, etc. And
patience - most of all!
Regards,
Matt Considine
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