Understanding Systematic Risk: A High-Frequency Approach

101 Pages Posted: 24 Aug 2015 Last revised: 16 May 2019

See all articles by Markus Pelger

Markus Pelger

Stanford University - Management Science & Engineering

Date Written: May 12, 2019


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 with principal component analysis applied to a local volatility and jump covariance matrix. I find four stable continuous systematic factors, which can be approximated very well 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

Suggested Citation

Pelger, Markus, Understanding Systematic Risk: A High-Frequency Approach (May 12, 2019). Journal of Finance, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2647040 or http://dx.doi.org/10.2139/ssrn.2647040

Markus Pelger (Contact Author)

Stanford University - Management Science & Engineering ( email )

473 Via Ortega
Stanford, CA 94305-9025
United States

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