Estimation of Characteristics-based Quantile Factor Models

Liang Chen, Juan Dolado, Jesus Gonzalo and Haozi Pan

No 18115, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

JEL-codes: C12 C33 (search for similar items in EconPapers)
Date: 2023-04
References: Add references at CitEc
Citations:

Downloads: (external link)
https://cepr.org/publications/DP18115 (application/pdf)
CEPR Discussion Papers are free to download for our researchers, subscribers and members. If you fall into one of these categories but have trouble downloading our papers, please contact us at subscribers@cepr.org

Related works:
Working Paper: Estimation of Characteristics-based Quantile Factor Models (2023)
Working Paper: Estimation of characteristics-based quantile factor models (2023)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:18115

Ordering information: This working paper can be ordered from
https://cepr.org/publications/DP18115

Access Statistics for this paper

More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX.
Bibliographic data for series maintained by ().