Understanding Systematic Risk: A High-Frequency Approach

Abstract

Based on a novel high-frequency data set for a large number of firms, I estimate the time-varying latent continuous and jump factors that explain individual stock returns. The factors are estimated using principal component analysis applied to a local volatility and jump covariance matrix. I find four stable continuous systematic factors, which can be well-approximated by a market, oil, finance, and electricity portfolio, while there is only one stable jump market factor. The exposure of stocks to these risk factors and their explained variation is time-varying. The four continuous factors carry an intraday risk premium that reverses overnight.

Keywords: Systematic Risk, High-dimensional Data, High-Frequency Data, Latent Factors, PCA, Jumps, Cross-Section of Returns, Time-Varying Risk, Industry Factors

JEL Classification: C38, C52, C55, C58, G12