A Fast Algorithm for Computing High-Dimensional Risk Parity Portfolios

9 Pages Posted: 15 Sep 2013 Last revised: 1 Oct 2013

See all articles by Théophile Griveau-Billion

Théophile Griveau-Billion

Imperial College London

Jean-Charles Richard

Eisler Capital

Thierry Roncalli

Amundi Asset Management; University of Evry

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

Suggested Citation

Griveau-Billion, Théophile and Richard, Jean-Charles and Roncalli, Thierry, A Fast Algorithm for Computing High-Dimensional Risk Parity Portfolios (September 1, 2013). Available at SSRN: https://ssrn.com/abstract=2325255 or http://dx.doi.org/10.2139/ssrn.2325255

Théophile Griveau-Billion

Imperial College London ( email )

South Kensington Campus
Exhibition Road
London, Greater London SW7 2AZ
United Kingdom

Jean-Charles Richard

Eisler Capital ( email )

16 St. James's Street
London, SW1A1ER

Thierry Roncalli (Contact Author)

Amundi Asset Management ( email )

90 Boulevard Pasteur
Paris, 75015

University of Evry ( email )

Boulevard Francois Mitterrand
F-91025 Evry Cedex

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics