A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios
Théophile Griveau-Billion, Jean-Charles Richard, Thierry Roncalli · Munich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2013
In this paper we propose a cyclical coordinate descent (CCD) algorithm for solving high dimensional risk parity problems. We show that this algorithm converges and is very fast even with large covariance matrices (n > 500). Comparison with existing algorithms also shows that it is one of the most efficient algorithms.