A Fast Algorithm for Computing High-Dimensional Risk Parity Portfolios
9 Pages Posted: 15 Sep 2013 Last revised: 1 Oct 2013
Date Written: September 1, 2013
In this paper we propose a coordinate descent 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.
Keywords: Risk parity, risk budgeting, ERC portfolio, cyclical coordinate descent algorithm, SQP algorithm, Jacobi algorithm, Newton algorithm, Nesterov algorithm
JEL Classification: G11, C60
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