A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios

Théophile Griveau-Billion, Jean-Charles Richard and Thierry Roncalli

MPRA Paper from University Library of Munich, Germany

Abstract: 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.

Keywords: Risk parity; risk budgeting; ERC portfolio; cyclical coordinate descent algorithm; lasso (search for similar items in EconPapers)
JEL-codes: C60 G11 (search for similar items in EconPapers)
Date: 2013-09-01
New Economics Papers: this item is included in nep-cmp, nep-ore and nep-rmg
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Citations: View citations in EconPapers (5)

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https://mpra.ub.uni-muenchen.de/49822/1/MPRA_paper_49822.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/49844/9/MPRA_paper_49844.pdf revised version (application/pdf)

Related works:
Working Paper: A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios (2013)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:49822

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