Understanding Systematic Risk: A High-Frequency Approach |
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| Authors: | MARKUS PELGER |
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| Institution: | 1. Markus Pelger is at the Department of Management Science & Engineering, Stanford University. I thank Jason Zhu for excellent research assistance. I thank Yacine Aït-Sahalia;2. Torben Andersen;3. Robert M. Anderson;4. Svetlana Bryzgalova;5. Mikhail Chernov;6. John Cochrane;7. Frank Diebold;8. Darrell Duffie;9. Noureddine El Karoui;10. Steve Evans;11. Jianqing Fan;12. Kay Giesecke;13. Lisa Goldberg;14. Valentin Haddad;15. Michael Jansson;16. Martin Lettau;17. Ulrike Malmendier;18. Stefan Nagel (Editor);19. Olivier Scaillet;20. Ken Singleton;21. George Tauchen;22. Viktor Todorov;23. Neil Shephard;24. Dacheng Xiu;25. two anonymous referees;26. and audience participants at UC Berkeley, Stanford, University of Pennsylvania, University of Bonn and SoFiE, INFORMS, FERM, Econometric society, and NBER Time-Series meetings. This work was supported by the Center for Risk Management Research at UC Berkeley. I have read The Journal of Finance disclosure policy and have no conflict of interest to 27. disclose. |
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| 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. |
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