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An Enhanced Factor Model for Portfolio Selection in High Dimensions

Author

Listed:
  • Fangquan Shi
  • Lianjie Shu
  • Xinhua Gu

Abstract

This article extends Fama and French (FF) models of observed factors by introducing latent factors (LFs) to further extract information from FF residual returns. A diagonally dominant (DD) rather than a diagonal or sparse matrix structure is adopted in this study to estimate remaining covariance between disturbance terms. Such an enhanced factor (EF) model provides a more comprehensive analysis for portfolio selection in high dimensions and also has certain advantages of estimation stability and computational efficiency. It is shown that the proposed EF–DD approach achieves overall better performance than competing models in terms of portfolio variance and the net Sharpe ratio.

Suggested Citation

  • Fangquan Shi & Lianjie Shu & Xinhua Gu, 2024. "An Enhanced Factor Model for Portfolio Selection in High Dimensions," Journal of Financial Econometrics, Oxford University Press, vol. 22(1), pages 94-118.
    • Handle: RePEc:oup:jfinec:v:22:y:2024:i:1:p:94-118.
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    File URL: http://hdl.handle.net/10.1093/jjfinec/nbac029
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    More about this item

    Keywords

    covariance matrices; diagonally dominant structures; factor models; Fama and French models; latent factors; minimum variance portfolios (MVPs);
    All these keywords.

    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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