Hello everybody,
sorry for my delayed "thanks" note - I was travelling.
@Arun: Debugging the underlying code is a little bit difficult since
the optimizer was written in FORTRAN. I think going for the nearest PD
(as Krishna also suggested) might be the best way. However, I honestly
don't understand why it is not PD... Does anybody have an explanation
for that?
@Guy: The weird thing is that I got the error code without the t(x) in
the first place. t(x) solved the problem (for some assets) and the
result indicated that it took a look at the assets and not the
observations... I am going to give it a try with the covariance matrix
again and let you know if it worked out... strange though.
@Krishna: Thanks for your link! I think that really helps... I am
going to try it out! Do you have an explanation why this is a common
problem with a large number of assets?
Thank you! Have a nice weekend!
Lui
On Fri, Jan 28, 2011 at 3:51 PM, krishna <kriskumar at earthlink.net>
wrote:
Hi Lui,
Without seeing the data this is just speculation but...
Are you sure you want t(x)? If you're mixing up your observations
versus
your assets this may explain the error.
The first parameter of portfolio.optim (in the tseries package) is a
returns matrix, one column for each asset and one row for each day
(assuming
daily returns). If you have this wrong then for your small
datasets you'd
have more columns than rows and this could produce that error.
Also, you don't have to pass the entire returns matrix to
portfolio.optim,
you could pass just the covariance matrix you calculate yourself
and a
vector (1-row matrix) of mean returns as follows:
library(tseries)
set.seed(2)
R <- matrix(rnorm(100*10),nrow=100,ncol=10) # 10 assets, 100
observations
averet <- matrix(apply(R,2,mean),nrow=1)
rcov <- cov(R)
current_er <- 0.05
(op <- portfolio.optim(x=averet,pm=current_er,covmat=rcov,riskless =
FALSE,shorts = FALSE, rf = 0.0))
Hope this helps.
Best,
Guy
On 1/26/2011 7:51 PM, Lui ## wrote:
Dear Group,
I have a large set of stocks and want to determine the efficient
frontier. The data set covers approx. 1.5 years and S&P 500
companies
(nothing weird). portfolio.optim from the PerformanceAnalytics
package
works very well and fast. However, whenever I decrease the number
of
stocks in the portfolio (to 10 or 400), I receive an error message:
"solve.QP(Dmat, dvec, Amat, bvec = b0, meq = 2) :
matrix D in quadratic function is not positive definite!"
My command settings for portfolio.optim were:
seed<- portfolio.optim(t(x), pm = current_er, riskless = FALSE,
shorts = FALSE, rf = 0.0)
Even when I tried it with shorts = TRUE the error would still
remain.
x is the set of stocks (stocks in columns, time in rows),
current_er
is the target return (lies between the minimal mean and the maximum
mean of a long only portfolio).
I can not post the stock data here - so maybe you have some general
suggestions for me of what could have gone wrong... The covariance
matrix is positive definite. What could cause the problem? It works
fine with the large data set but does not work at all with the
small
one...
Thanks a lot for your suggestions!
Lui