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Reduction of estimation error impact in the risk parity strategies

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  • Hyuksoo Kim
  • Saejoon Kim

Abstract

We consider the risk parity strategy in the presence of estimation errors. We show that risk contributions from constituents of this portfolio can be considerably sensitive to estimation errors in the sense that risk contributions are highly uneven on an ex post basis. In particular, we demonstrate that the sensitivity becomes exaggerated if Fama-French factors constitute the portfolio because of their characteristic of having low pairwise correlations. Our work demonstrates that the instability of the out-of-sample risk contributions is associated with a local property with statistical significance near to the constructed portfolio. Based on this observation, we propose a new algorithm for the risk parity strategy to mitigate the sensitivity of the optimized portfolio's out-of-sample risk contributions from estimation errors. Through empirical study, we find that the portfolio constructed by the proposed algorithm consistently outperforms its competitors in terms of the out-of-sample risk contributions.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:8:p:1351-1364
    DOI: 10.1080/14697688.2021.1881599
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    Cited by:

    1. Jaehyuk Choi & Rong Chen, 2022. "Improved iterative methods for solving risk parity portfolio," Papers 2203.00148, arXiv.org.

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