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Robust estimation of the number of factors for the pair-elliptical factor models

Author

Listed:
  • Shuquan Yang

    (Hefei University of Technology)

  • Nengxiang Ling

    (Hefei University of Technology)

  • Yulin Gong

    (Macau University of Science and Technology)

Abstract

In this paper, we investigate the robust estimation of the number of common factors in high-dimensional factor model with pair-elliptically distributed idiosyncratic errors. Motivated by the pandemic heavy-tail distributions of financial returns, we first introduce a pair-elliptical factor model by allowing the factors and noises to follow pairwisely the joint elliptical distributions. Compared with the elliptical factor model invented in Fan et al. (Ann Stat 46:1383–1414, 2018), the pair-elliptical factor model has more richer structure with more relaxed assumptions. We propose two robust quantile-based estimators of the number of factors and obtain the asymptotic properties of the estimators under some mild conditions. Then, some simulation studies and a real data analysis are carried out to show the effectiveness of the estimators of the factor numbers.

Suggested Citation

  • Shuquan Yang & Nengxiang Ling & Yulin Gong, 2022. "Robust estimation of the number of factors for the pair-elliptical factor models," Computational Statistics, Springer, vol. 37(3), pages 1495-1522, July.
    • Handle: RePEc:spr:compst:v:37:y:2022:i:3:d:10.1007_s00180-021-01165-5
    DOI: 10.1007/s00180-021-01165-5
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    References listed on IDEAS

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