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Estimation of Characteristics-based Quantile Factor Models

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  • Liang Chen
  • Juan Jose Dolado
  • Jesus Gonzalo
  • Haozi Pan

Abstract

This paper studies the estimation of characteristic-based quantile factor models where the factor loadings are unknown functions of observed individual characteristics while the idiosyncratic error terms are subject to conditional quantile restrictions. We propose a three-stage estimation procedure that is easily implementable in practice and has nice properties. The convergence rates, the limiting distributions of the estimated factors and loading functions, and a consistent selection criterion for the number of factors at each quantile are derived under general conditions. The proposed estimation methodology is shown to work satisfactorily when: (i) the idiosyncratic errors have heavy tails, (ii) the time dimension of the panel dataset is not large, and (iii) the number of factors exceeds the number of characteristics. Finite sample simulations and an empirical application aimed at estimating the loading functions of the daily returns of a large panel of S\&P500 index securities help illustrate these properties.

Suggested Citation

  • Liang Chen & Juan Jose Dolado & Jesus Gonzalo & Haozi Pan, 2023. "Estimation of Characteristics-based Quantile Factor Models," Papers 2304.13206, arXiv.org.
    • Handle: RePEc:arx:papers:2304.13206
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    More about this item

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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