Correlation (cor) vs. Covariance (cov)

Hi all,

does someone know why to compute a loop with the cor function takes way longer than doing it with the cov function?

I've the followin loop

covC = cell(szX(1),1);
CovM = zeros(szX(1),szX(2));
for n = ws1+1:szX(1)
    covC{n} = cov(((logr(n-ws1:n,:))));
    CovM(n,:)= (covC{n}(1,:));
end

the matrix is ca. (1580x32)... if u run the posted loop it takes just a few seconds. However If I want to compute the correlation (and substitute cov with cor) it takes about 5 minutes

corC = cell(szX(1),1);
CorM = zeros(szX(1),szX(2));
for n = ws1+1:szX(1)
    corC{n} = corr(((logr(n-ws1:n,:))));
    CorM(n,:)= (corC{n}(1,:));
end

. I want to do it also with a 3000x1200 matrix

can someone suggest me a quicker prodecure to achive my goal?

thaks for ur hepl

[Newuser]

Problem solved by using corrcoef

[Oleg Komarov]

You can use the compiler to verify where are the bottlenecks in corr as compared to corrcoeff.