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High dimensional mean–variance optimization through factor analysis

Author

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  • Chen, Binbin
  • Huang, Shih-Feng
  • Pan, Guangming

Abstract

A factor analysis-based approach for estimating high dimensional covariance matrix is proposed and is applied to solve the mean–variance portfolio optimization problem in finance. The consistency of the proposed estimator is established by imposing a factor model structure with a relative weak assumption on the relationship between the dimension and the sample size. Numerical results indicate that the proposed estimator outperforms the plug-in, linear shrinkage and bootstrap-corrected approaches.

Suggested Citation

  • Chen, Binbin & Huang, Shih-Feng & Pan, Guangming, 2015. "High dimensional mean–variance optimization through factor analysis," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 140-159.
    • Handle: RePEc:eee:jmvana:v:133:y:2015:i:c:p:140-159
    DOI: 10.1016/j.jmva.2014.09.006
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    References listed on IDEAS

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    Cited by:

    1. Sun, Xuelian & Liu, Zixian, 2016. "Optimal portfolio strategy with cross-correlation matrix composed by DCCA coefficients: Evidence from the Chinese stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 667-679.
    2. Andrew Butler & Roy H. Kwon, 2021. "Integrating prediction in mean-variance portfolio optimization," Papers 2102.09287, arXiv.org, revised Nov 2022.

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