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ABSTRACT

This paper proposes a method to capture variation in the factor structure of asset returns within a trade day by combining non-parametric kernel methods with principal component analysis. We estimate our “Intraday PCA” on a collection of over 400 high frequency U.S. equity returns over the period 1996-2020 and show that the proposed model has superior explanatory power, both in-sample and out-of-sample, economically as well as statistically, relative to a collection of well-known observable factor models and standard PCA. Using data on individual firm earnings announcements, FOMC announcements, and after-hours realized volatility, we provide evidence that the superior performance of the proposed model is due to time variation in the factor structure of asset returns around times of information flows.

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