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Covariance Matrix Estimation in Time-Varying Factor Models

Author

Listed:
  • Yao Jingming

    (12544 Shanghai Normal University , Shanghai 200234, China)

  • Wu Jianhong

    (12544 Shanghai Normal University , Shanghai 200234, China)

Abstract

This paper considers the covariance matrix estimation in time-varying factor models. A so-called two-step method is proposed to estimate the covariance matrix, which is shown to be more accurate for high-dimensional data. Simulation results show that the proposed method has desired performance under finite samples, especially for the case with rapidly changing factor loadings. The empirical study shows that the minimum variance portfolio constructed by this method has desired performance in terms of both return and risk control.

Suggested Citation

  • Yao Jingming & Wu Jianhong, 2026. "Covariance Matrix Estimation in Time-Varying Factor Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 30(3), pages 485-501.
    • Handle: RePEc:bpj:sndecm:v:30:y:2026:i:3:p:485-501:n:1012
    DOI: 10.1515/snde-2025-0042
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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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