You can use a factor model or shrinkage to get a positive definite variance matrix. There is a function for each in the BurStFin package on CRAN.
The optimizer in Portfolio Probe doesn't care about positive definiteness (though that is not always a good thing). It is free for academic use. Pat On 22/09/2015 14:37, aschmid1 wrote:
Hi everyone, I'm trying to estimate optimal Markowitz portfolio weights for a list of stocks chosen upon some criterion using solve.QP from quadprog library. When the number of stocks N reaches some limit, I get a message "matrix D in quadratic function is not positive definite." For example, if I rebalance every 6 weeks (which implies that variance is calculated for 6-week interval prior to the period for which I calculate portfolio weights), I can get solution for 25>=N<50. For 12-week interval, solution exists for 50>=N<100, and for 24-week interval, I can get solution for N=100. My attempt to remedy this problem with Higham's method doesn't help. I'll greatly appreciate you input: first, why this may happen (can there be lack of local minimum?), and second, whether there are R solvers that may need only semi positive definite matrix. Thanks! Alec _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
-- Patrick Burns [email protected] http://www.burns-stat.com http://www.portfolioprobe.com/blog twitter: @burnsstat @portfolioprobe _______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-sig-finance -- Subscriber-posting only. If you want to post, subscribe first. -- Also note that this is not the r-help list where general R questions should go.
