[R] Re: Help : generating correlation matrix with a particular

Herbert_Desson@jltgroup.com Herbert_Desson at jltgroup.com
Mon Dec 13 13:24:10 CET 2004

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Here is some code we have used.

for(i in 1:nrow(b)){b[i,]<-b[i,]/sqrt(sum(b[i,]^2))}

It is based on a paper by Rebonato etal that formerly was at
Unfortunately the website has disappeared.

Best regards,

Herbert G. Desson, ACAS, MAAA

JLT Risk Solutions
6 Crutched Friars
London EC3N 2PH

phone:  +44 (0)20 7528 4702
fax:       +44 (0)20 7558 3785

>Message: 2
>Date: 12 Dec 2004 14:58:38 +0100
>From: Peter Dalgaard <p.dalgaard at biostat.ku.dk>
>Subject: Re: [R] Help : generating correlation matrix with a
>	particular	structure
>To: Siew Leng TENG <siewlengteng at yahoo.com>
>Cc: r-help at stat.math.ethz.ch
>Message-ID: <x2sm6bd3e9.fsf at biostat.ku.dk>
>Content-Type: text/plain; charset=us-ascii
>Siew Leng TENG <siewlengteng at yahoo.com> writes:
>> Hi,
>> I would like to generate a correlation matrix with a
>> particular structure. For example, a 3n x 3n matrix :
>> A_(nxn)   aI_(nxn)  bI_(nxn)
>> aI_(nxn)  A_(nxn)   cI_(nxn)
>> aI_(nxn)  cI_(nxn)  A_(nxn)
>> where
>> - A_(nxn) is a *specified* symmetric, positive
>> definite nxn matrix.
>> - I_(nxn) is an identity matrix of order n
>> - a, b, c are (any) real numbers
>> Many attempts have been unsuccessful because a
>> resulting matrix with any a, b, c may not be a
>> positive definite one, and hence cannot qualify as a
>> correlation matrix. Trying to first generate a
>> covariance matrix however, does not guarantee a
>> corresponding correlation matrix with the above
>> structure.
>Er, a correlation matrix *is* a covariance matrix with 1 down the
>You need to sort out the parametrization issues. What you're trying to
>achieve is quite hard. Consider the simpler case of two blocks and
>n=2; what you're asking for is a covariance matrix of the form
>1 r a 0
>r 1 0 a
>a 0 1 r
>0 a r 1
>so if this is the correlation matrix of (X1,Y1,X2,Y2) you want
>X1 and Y1 correlated 
>X2 and Y2 correlated
>X1 and X2 correlated
>Y1 and Y2 correlated
>X1 and Y2 uncorrelated
>Y1 and X2 uncorrelated
>One approach is to work out the conditional variance of (X2,Y2) given
>(X1,Y1) and check for positive semidefiniteness. You do the math...
>(Some preliminary experiments suggest that the criterion could be
>abs(a)+abs(r) <= 1, but don't take my word for it)
>> R-version used :
>> ---------------
>> Windows version
>> R-1.8.1
>> Running on Windows XP
>You might want to upgrade, but it might not do anything for you in
>this respect.
>   O__  ---- Peter Dalgaard             Blegdamsvej 3  
>  c/ /'_ --- Dept. of Biostatistics     2200 Cph. N   
> (*) \(*) -- University of Copenhagen   Denmark      Ph: (+45) 35327918
>~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk)             FAX: (+45) 35327907

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