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The impact of covariance misspecification in risk-based portfolios

Author

Listed:
  • David Ardia

    (University of Neuchâtel
    Université Laval)

  • Guido Bolliger

    (University of Neuchâtel
    Syz Asset Management (Suisse) SA)

  • Kris Boudt

    (Vrije Universiteit Brussel
    Vrije Universiteit Amsterdam)

  • Jean-Philippe Gagnon-Fleury

    (Université Laval)

Abstract

The equal-risk-contribution, inverse-volatility weighted, maximum-diversification and minimum-variance portfolio weights are all direct functions of the estimated covariance matrix. We perform a Monte Carlo study to assess the impact of covariance matrix misspecification to these risk-based portfolios at the daily, weekly and monthly forecasting horizon. Our results show that the equal-risk-contribution and inverse-volatility weighted portfolio weights are relatively robust to covariance misspecification. In contrast, the minimum-variance portfolio weights are highly sensitive to errors in both the estimated variances and correlations, while errors in the estimated correlations can have a large effect on the weights of the maximum-diversification portfolio.

Suggested Citation

  • David Ardia & Guido Bolliger & Kris Boudt & Jean-Philippe Gagnon-Fleury, 2017. "The impact of covariance misspecification in risk-based portfolios," Annals of Operations Research, Springer, vol. 254(1), pages 1-16, July.
    • Handle: RePEc:spr:annopr:v:254:y:2017:i:1:d:10.1007_s10479-017-2474-7
    DOI: 10.1007/s10479-017-2474-7
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    4. Kei Nakagawa & Shuhei Noma & Masaya Abe, 2020. "RM-CVaR: Regularized Multiple $\beta$-CVaR Portfolio," Papers 2004.13347, arXiv.org, revised May 2020.
    5. Zhang, Xi & Li, Jian, 2018. "Credit and market risks measurement in carbon financing for Chinese banks," Energy Economics, Elsevier, vol. 76(C), pages 549-557.
    6. Prayut Jain & Shashi Jain, 2019. "Can Machine Learning-Based Portfolios Outperform Traditional Risk-Based Portfolios? The Need to Account for Covariance Misspecification," Risks, MDPI, vol. 7(3), pages 1-27, July.
    7. Thomas Conlon & John Cotter & Iason Kynigakis, 2021. "Machine Learning and Factor-Based Portfolio Optimization," Working Papers 202111, Geary Institute, University College Dublin.
    8. Li, Xiaoyue & Uysal, A. Sinem & Mulvey, John M., 2022. "Multi-period portfolio optimization using model predictive control with mean-variance and risk parity frameworks," European Journal of Operational Research, Elsevier, vol. 299(3), pages 1158-1176.
    9. Ardia, David & Boudt, Kris & Hartmann, Stefan & Nguyen, Giang, 2022. "Properties of the Margrabe Best-of-two strategy to tactical asset allocation," International Review of Financial Analysis, Elsevier, vol. 81(C).
    10. Giorgio Costa & Roy Kwon, 2020. "A robust framework for risk parity portfolios," Journal of Asset Management, Palgrave Macmillan, vol. 21(5), pages 447-466, September.
    11. Lu, Cheng & Ndiaye, Papa Momar & Simaan, Majeed, 2024. "Improved estimation of the correlation matrix using reinforcement learning and text-based networks," International Review of Financial Analysis, Elsevier, vol. 96(PA).
    12. Caldeira, João F. & Santos, André A.P. & Torrent, Hudson S., 2023. "Semiparametric portfolios: Improving portfolio performance by exploiting non-linearities in firm characteristics," Economic Modelling, Elsevier, vol. 122(C).
    13. Ayse Sinem Uysal & Xiaoyue Li & John M. Mulvey, 2021. "End-to-End Risk Budgeting Portfolio Optimization with Neural Networks," Papers 2107.04636, arXiv.org.
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    15. A. Sinem Uysal & Xiaoyue Li & John M. Mulvey, 2024. "End-to-end risk budgeting portfolio optimization with neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 397-426, August.
    16. Kei Nakagawa & Mitsuyoshi Imamura & Kenichi Yoshida, 2018. "Risk-Based Portfolios with Large Dynamic Covariance Matrices," IJFS, MDPI, vol. 6(2), pages 1-14, May.
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