[R-SIG-Finance] portfolio.optim and error in solve.QP: matrix D not positive definite
gyollin at r-programming.org
Thu Jan 27 18:03:16 CET 2011
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
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:
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.
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
> Thanks a lot for your suggestions!
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