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A Fast Algorithm for Computing High-dimensional Risk Parity Portfolios

Author

Listed:
  • Griveau-Billion, Théophile
  • Richard, Jean-Charles
  • Roncalli, Thierry

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.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:49822
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    File URL: https://mpra.ub.uni-muenchen.de/49844/9/MPRA_paper_49844.pdf
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    References listed on IDEAS

    as
    1. Roncalli, Thierry, 2013. "Introduction to Risk Parity and Budgeting," MPRA Paper 47679, University Library of Munich, Germany.
    2. Roncalli, Thierry, 2013. "Introducing Expected Returns into Risk Parity Portfolios: A New Framework for Tactical and Strategic Asset Allocation," MPRA Paper 49821, University Library of Munich, Germany.
    3. P. Tseng, 2001. "Convergence of a Block Coordinate Descent Method for Nondifferentiable Minimization," Journal of Optimization Theory and Applications, Springer, vol. 109(3), pages 475-494, June.
    4. Bruder, Benjamin & Roncalli, Thierry, 2012. "Managing risk exposures using the risk budgeting approach," MPRA Paper 37246, University Library of Munich, Germany.
    5. repec:dau:papers:123456789/4688 is not listed on IDEAS
    6. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    7. Cazalet, Zelia & Grison, Pierre & Roncalli, Thierry, 2013. "The Smart Beta Indexing Puzzle," MPRA Paper 48823, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Sarah Perrin & Thierry Roncalli, 2019. "Machine Learning Optimization Algorithms & Portfolio Allocation," Papers 1909.10233, arXiv.org.
    2. da Costa, B. Freitas Paulo & Pesenti, Silvana M. & Targino, Rodrigo S., 2023. "Risk budgeting portfolios from simulations," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1040-1056.
    3. Jaehyuk Choi & Rong Chen, 2022. "Improved iterative methods for solving risk parity portfolio," Papers 2203.00148, arXiv.org.
    4. Jean-Charles Richard & Thierry Roncalli, 2019. "Constrained Risk Budgeting Portfolios: Theory, Algorithms, Applications & Puzzles," Papers 1902.05710, arXiv.org.

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    More about this item

    Keywords

    Risk parity; risk budgeting; ERC portfolio; cyclical coordinate descent algorithm; lasso;
    All these keywords.

    JEL classification:

    • C60 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - General
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

    NEP fields

    This paper has been announced in the following NEP Reports:

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