  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.
