Hi Stephen:

It's the inputs given to me by a end-user. Ultimately trying to fit a student-t copula to a bunch of simulated price returns
while maintaining the structure of the estimated correlation matrix.

The other challenge is I use R to test and work a solution but then have also done in matlab as I had to write a  C# wrapper around the code for the end-user.

In a way I am stuck with Matalb but was hoping to try and solve with R which is great.

I appreciate your responding to my email help request.

Neil

________________________________
From: stenka1@go.com [mailto:stenka1@go.com]
Sent: Wednesday, April 01, 2009 10:47 AM
To: Gottlieb, Neil; r-help@r-project.org
Subject: Re: [R] Need Advice on Matrix Not Positive Semi-Definite with cholesky decomposition

Neil,

pls tell why do you need the correlation matrix? if you are trying to simulate correlated variables then you can go around the cholesky by using svd.
if you really need the correlation ( I think it is always possible to avoid it ) then Rmetrics have a function to turn your matrix into positive semi-definite.
you can also consider a factor model which will reduce the dimensionality tremendously.

Stephen C. Bond



On Apr 1, 2009, ngottlieb@marinercapital.com wrote:

Dear fellow R Users:

I am doing a Cholesky decomposition on a correlation matrix and get error message
the matrix is not semi-definite.

Does anyone know:
1- a work around to this issue?
2- Is there any approach to try and figure out what vector might be co-linear with another in thr Matrix?
3- any way to perturb the data to work around this?

Thanks for any suggestions.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

	[[alternative HTML version deleted]]


