It follows a code that I have developed during some other works. It generates !size series randomly distributed according to a multivariate normal with the covariance matrix also generated randomly. This may be changed to generate according to the desired covariance matrix. Just make sure that the cov. matrix is positive semi-definite.

It might be useful for some...

Cheers!

Code: Select all

`!size=3`

matrix(!size,!size) rnd_covs

call rnd_pos_def_mat(rnd_covs)

'' I was having some trouble with the near singular matrix problem. This while was my best solution for now...

while @issingular(@implode(rnd_covs))=1 ''

call rnd_pos_def_mat(rnd_covs)

wend

call rnd_multivariate_normal(rnd_covs)

subroutine local rnd_pos_def_mat(matrix mat_res)

!size=@columns(mat_res)

matrix(!size,!size) tmp=0

for !i=1 to !size

tmp(!i,!i)=@sqrt(@rfdist(100,100))

for !j=!i+1 to !size

tmp(!i,!j)=@nrnd

next

next

matrix mat_res=@transpose(tmp)*tmp

endsub

subroutine rnd_multivariate_normal(matrix covs)

sym covs_sym=@implode(covs)

matrix cholesky=@cholesky(covs_sym)

matrix(@columns(covs),@obssmpl) vz_mat

for !i=1 to @columns(covs)

series z_norm=nrnd

stom(z_norm,vz_norm)

rowplace(vz_mat,@transpose(vz_norm),!i)

delete z_norm vz_norm

next

matrix mat_xs=cholesky*vz_mat

matrix mat_xs=@transpose(mat_xs)

mtos(mat_xs,xs)

delete covs_sym cholesky vz_mat mat_xs

endsub