A New Method for Generating Random Correlation Matrices
Ilya Archakov, Peter Reinhard Hansen, Yiyao Luo · RePEc: Research Papers in Economics · 2022
We propose a new method for generating random correlation matrices that makes it simple to control both location and dispersion. The method is based on a vector parameterization, gamma = g(C), which maps any distribution on R^d, d = n(n-1)/2 to a distribution on the space of non-singular nxn correlation matrices. Correlation matrices with certain properties, such as being well-conditioned, having block structures, and having strictly positive elements, are simple to generate. We compare the new method with existing methods.