Measuring and Testing the Impact of News on Volatility

Authors

  • ROBERT F. ENGLE,

  • VICTOR K. NG

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    • Engle is from the Department of Economics, University of California, San Diego, and Ng is from the School of Business Administration, University of Michigan, Ann Arbor. We thank John Campbell, David Hsieh, Gautam Kaul, Laura Kodres, Stanley Kon, M. P. Narayanan, Bill Schwert, Paul Seguin, Rob Stambaugh, René Stulz, an anonymous referee, and workshop participants at the University of California, San Diego, the University of Michigan, the University of Southern California and conference participants at the National Bureau of Economic Research, Inc., and the Southern Finance Association Meetings for helpful comments. We also thank Zhuanxin Ding for his excellent research assistance in carrying out the simulation experiment in this paper.


ABSTRACT

This paper defines the news impact curve which measures how new information is incorporated into volatility estimates. Various new and existing ARCH models including a partially nonparametric one are compared and estimated with daily Japanese stock return data. New diagnostic tests are presented which emphasize the asymmetry of the volatility response to news. Our results suggest that the model by Glosten, Jagannathan, and Runkle is the best parametric model. The EGARCH also can capture most of the asymmetry; however, there is evidence that the variability of the conditional variance implied by the EGARCH is too high.

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