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Improved iterative methods for solving risk parity portfolio

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  • Jaehyuk Choi
  • Rong Chen

Abstract

Risk parity, also known as equal risk contribution, has recently gained increasing attention as a portfolio allocation method. However, solving portfolio weights must resort to numerical methods as the analytic solution is not available. This study improves two existing iterative methods: the cyclical coordinate descent (CCD) and Newton methods. We enhance the CCD method by simplifying the formulation using a correlation matrix and imposing an additional rescaling step. We also suggest an improved initial guess inspired by the CCD method for the Newton method. Numerical experiments show that the improved CCD method performs the best and is approximately three times faster than the original CCD method, saving more than 40% of the iterations.

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  • Jaehyuk Choi & Rong Chen, 2022. "Improved iterative methods for solving risk parity portfolio," Papers 2203.00148, arXiv.org.
    • Handle: RePEc:arx:papers:2203.00148
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    References listed on IDEAS

    as
    1. Hyuksoo Kim & Saejoon Kim, 2021. "Reduction of estimation error impact in the risk parity strategies," Quantitative Finance, Taylor & Francis Journals, vol. 21(8), pages 1351-1364, August.
    2. Griveau-Billion, Théophile & Richard, Jean-Charles & Roncalli, Thierry, 2013. "A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios," MPRA Paper 49822, University Library of Munich, Germany.
    3. repec:dau:papers:123456789/4688 is not listed on IDEAS
    4. Xi Bai & Katya Scheinberg & Reha Tutuncu, 2016. "Least-squares approach to risk parity in portfolio selection," Quantitative Finance, Taylor & Francis Journals, vol. 16(3), pages 357-376, March.
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