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A robust latent factor model for high-dimensional portfolio selection

Author

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  • Shi, Fangquan
  • Shu, Lianjie
  • Gu, Xinhua

Abstract

Portfolio selection, faced with large volatile data sets of strongly correlated asset returns, is prone to unstable portfolio weights and serious estimation error. To attenuate this problem, our work proposes a new latent factor model equipped with both a suitable robust estimator to deal with cellwise data contamination and a diagonally-dominant (DD) covariance structure to account for cross-sectional dependence among residual returns. The proposed robust DD model is found to compare favorably with various competitors from the literature in terms of out-of-sample portfolio performance across real-world data sets.

Suggested Citation

  • Shi, Fangquan & Shu, Lianjie & Gu, Xinhua, 2025. "A robust latent factor model for high-dimensional portfolio selection," Journal of Empirical Finance, Elsevier, vol. 83(C).
    • Handle: RePEc:eee:empfin:v:83:y:2025:i:c:s0927539825000453
    DOI: 10.1016/j.jempfin.2025.101623
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    References listed on IDEAS

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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