One correction and another comment.
I should have said that a trade optimizer
not caring about positive definiteness is
usually a bad thing -- it has its uses
but giving the optimizer a shot at a
portfolio with negative variance is asking
for trouble (because it will do its best
to get there).
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
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,
Because you can't invert a matrix with more features than observations. IE
if you have 50 stocks but only use 26 weekly return data points, you can't
invert that covariance matrix because it's not positive semi-definite. You
may want to try my Constrained Critical Line algorithm.
On Tue, 22 Sep 2015, aschmid1 writes:
> 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