Estimation of characteristics-based quantile factor models

Liang Chen and Haozi Pan
Authors registered in the RePEc Author Service: Jesus Gonzalo and Juan J. Dolado

UC3M Working papers. Economics from Universidad Carlos III de Madrid. Departamento de Economía

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 proposedestimation 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.

Keywords: Quantile; Factor; Models; Nonparametric; Quantile; Regression; Principal; Component; Analysis (search for similar items in EconPapers)
Date: 2023-04-14
New Economics Papers: this item is included in nep-dcm and nep-ecm
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Related works:
Working Paper: Estimation of Characteristics-based Quantile Factor Models (2023)
Working Paper: Estimation of Characteristics-based Quantile Factor Models (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:cte:werepe:37095

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