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Factor Models and Covariance Matrices

In: Covariance Analysis and Beyond

Author

Listed:
  • Wei Lan

    (Southwestern University of Finance and Economics, School of Statistics and Data Science and Center of Statistical Research)

  • Chih-Ling Tsai

    (University of California - Davis, Graduate School of Management)

Abstract

Factor modelsFactor models involve a reduction of parameters in the covariance matrix, and they are highly related to principal componentsPrincipal components. Since these models have played a very important role in data analysis, we not only introduce classical factor modelsFactor models but also include four extended factor modelsFactor models, namely approximate factor modelsApproximate factor (AF) model, factor-augmented regression modelsFactor-augmented regression (FAR) model, dynamic factor modelsDynamic factor model (DFM), and matrix factor modelsMatrix factor models. In addition, three examples are presented to briefly illustrate empirical applications.

Suggested Citation

  • Wei Lan & Chih-Ling Tsai, 2026. "Factor Models and Covariance Matrices," Springer Books, in: Covariance Analysis and Beyond, chapter 0, pages 121-137, Springer.
    • Handle: RePEc:spr:sprchp:978-3-032-08796-6_8
    DOI: 10.1007/978-3-032-08796-6_8
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