Working Paper: CEPR ID: DP18115
Authors: Liang Chen; Juan J. 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.
Keywords: quantile factor models; nonparametric quantile regression; principal component analysis
JEL Codes: C12; C33
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Idiosyncratic errors in CQFM (C20) | Understanding of how different characteristics influence asset returns across various quantiles (C22) |
| Quantile-projected principal components analysis (QPPCA) (C38) | Consistent estimation of the number of factors at each quantile (C51) |
| Quantile-projected principal components analysis (QPPCA) (C38) | Identification of substantial variations in loading functions across quantiles (C22) |
| Quantile-projected principal components analysis (QPPCA) (C38) | Robustness to heavy tails and outliers compared to previous methods (C46) |
| Deviations from the efficient market hypothesis (G14) | Different relevance for factors contributing to alpha generation based on the distribution of excess returns (C46) |