Estimation and Inference in Semiparametric Quantile Factor Models

Working Paper
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Ma, S., Linton, O., Gao, J. 
Linton, O. 
Gao, J. 

We consider a semiparametric quantile factor panel model that allows observed stock-specific characteristics to affect stock returns in a nonlinear time-varying way, extending Connor, Hagmann, and Linton (2012) to the quantile restriction case. We propose a sieve-based estimation methodology that is easy to implement. We provide tools for inference that are robust to the existence of moments and to the form of weak cross-sectional dependence in the idiosyncratic error term. We apply our method to daily stock return data where we find significant evidence of nonlinearity in many of the characteristic exposure curves.

Cross-Sectional Dependence, Fama-French Model, Inference, Quantile, Sieve Estimation
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