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Common Factors and Specific Factors

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  • Chen, Pu

Abstract

In this paper we study factor models for security returns on financial markets, where some pervasive factors are common across all securities and other pervasive factors prevail only within some groups of securities but not in others. This kind of structured factors allow a more nuanced analysis of determinants of the security returns, in particular, they allow to study clustering structures in security returns as well as their determinants. The clustering structure provides a natural way to group the securities and to interpret common factors and group-specific factors. We give conditions under which the common factor space and the group-specific factor spaces can be identified, and propose an effective procedure to estimate the unobservable structure in the factor space. Concretely, the procedure will determine the unknown number of groups, endogenously classify securities into groups, determine the number of common factors across all groups as well as the number of group-specific factors in each group, and estimate the common factors and the group-specific factors. The estimated factor structure will provides a more meaningful interpretation of the estimated factors in practical applications.

Suggested Citation

  • Chen, Pu, 2012. "Common Factors and Specific Factors," MPRA Paper 36085, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:36085
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    File URL: https://mpra.ub.uni-muenchen.de/36114/1/MPRA_paper_36114.pdf
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    References listed on IDEAS

    as
    1. Stephen A. Ross, 2013. "The Arbitrage Theory of Capital Asset Pricing," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 1, pages 11-30, World Scientific Publishing Co. Pte. Ltd..
    2. Goyal, Amit & Pérignon, Christophe & Villa, Christophe, 2008. "How common are common return factors across the NYSE and Nasdaq?," Journal of Financial Economics, Elsevier, vol. 90(3), pages 252-271, December.
    3. Boivin, Jean & Ng, Serena, 2006. "Are more data always better for factor analysis?," Journal of Econometrics, Elsevier, vol. 132(1), pages 169-194, May.
    4. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    5. Chen, Pu, 2010. "A Grouped Factor Model," MPRA Paper 28083, University Library of Munich, Germany, revised 11 Jan 2011.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. In Choi & Rui Lin & Yongcheol Shin, 2020. "Online Appendix for Canonical Correlation-based Model Selection for the Multilevel Factors," Working Papers 2008, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    2. Venetis, Ioannis & Ladas, Avgoustinos, 2022. "Co-movement and global factors in sovereign bond yields," MPRA Paper 115801, University Library of Munich, Germany.
    3. Choi, In & Lin, Rui & Shin, Yongcheol, 2023. "Canonical correlation-based model selection for the multilevel factors," Journal of Econometrics, Elsevier, vol. 233(1), pages 22-44.

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    More about this item

    Keywords

    Factor Models; Generalized Principal Component Analysis; Model Selection; Multiset Canonical Correlation;
    All these keywords.

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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