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Improving Minimum-Variance Portfolios by Alleviating Overdispersion of Eigenvalues

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  • Shi, Fangquan
  • Shu, Lianjie
  • Yang, Aijun
  • He, Fangyi

Abstract

In portfolio risk minimization, the inverse covariance matrix of returns is often unknown and has to be estimated in practice. Yet the eigenvalues of the sample covariance matrix are often overdispersed, leading to severe estimation errors in the inverse covariance matrix. To deal with this problem, we propose a general framework by shrinking the sample eigenvalues based on the Schatten norm. The proposed framework has the advantage of being computationally efficient as well as structure-free. The comparative studies show that our approach behaves reasonably well in terms of reducing out-of-sample portfolio risk and turnover.

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  • Shi, Fangquan & Shu, Lianjie & Yang, Aijun & He, Fangyi, 2020. "Improving Minimum-Variance Portfolios by Alleviating Overdispersion of Eigenvalues," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 55(8), pages 2700-2731, December.
    • Handle: RePEc:cup:jfinqa:v:55:y:2020:i:8:p:2700-2731_10
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    Cited by:

    1. Lim Hao Shen Keith, 2024. "Covariance Matrix Analysis for Optimal Portfolio Selection," Papers 2407.08748, arXiv.org.
    2. Tu, Xueyong & Li, Bin, 2024. "Robust portfolio selection with smart return prediction," Economic Modelling, Elsevier, vol. 135(C).

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