Does one use the covariance or correlation matrix in cholesky decomposition to generate correlated samples
Can we interchangeably use Cholesky decomposition of covariance and correlation matrix to generate simulations? If not, in which situations do we use one or the other and why? Thanks in advance.
Large (5K-10K) non positive definite (particularly near singular) covariance matrices and treatments for Cholesky decomposition
I have a very large covariance matrix (around 10000x10000) of returns, which is constructed using a sample size of 1000 for 10000 variables. My goal is to perform a (good-looking) Cholesky ...