High-frequency factor models and regressions

We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all com...

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Bibliographic Details
Published in:Journal of econometrics 2020-05, Vol.216 (1), p.86-105
Main Authors: Aït-Sahalia, Yacine, Kalnina, Ilze, Xiu, Dacheng
Format: Article
Language:English
Subjects:
Big data
Earnings
Estimating techniques
Factor model
Fama–French factors
Idiosyncratic risk
Regression analysis
Securities markets
Stocks
Time series
Time-varying betas
Volatility
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Summary:We consider a nonparametric time series regression model. Our framework allows precise estimation of betas without the usual assumption of betas being piecewise constant. This property makes our framework particularly suitable to study individual stocks. We provide an inference framework for all components of the model, including idiosyncratic volatility and idiosyncratic jumps. Our empirical analysis investigates the largest dataset in the high-frequency literature. First, we use all traded stocks from NYSE, AMEX, and NASDAQ stock markets for 1996–2017 to construct the five Fama–French factors and the momentum factor at the 5-minute frequency. Second, we document the key empirical properties across all the stocks and the new factors, and apply the nonparametric time series regression model with the new high-frequency Fama–French factors. We find that this factor model is effective in explaining the systematic component of the risk of individual stocks. In addition, we provide evidence that idiosyncratic jumps are related to idiosyncratic events such as earnings disappointments.
ISSN:0304-4076
1872-6895
DOI:10.1016/j.jeconom.2020.01.007