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News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns


  • John M. Maheu,

  • Thomas H. McCurdy

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    • Maheu is from the Department of Economics, University of Toronto, and McCurdy is from the Joseph L. Rotman School of Management, University of Toronto, and is an Associated Fellow of CIRANO. We are grateful to the editor (Rick Green) and an anonymous referee for very helpful comments. We also thank Toby Daglish, J.C. Duan, Robert Elliot, Adlai Fisher, and Nour Meddahi, as well as participants at the Modeling, Estimating and Forecasting Volatility Conference (University of Montreal), the Financial Mathematics Seminar (Fields Institute), the North American Summer Meeting of the Econometric Society (Los Angeles), the Northern Finance Association 2002 Meetings, the Canadian Econometrics Study Group, the Waterloo Financial Econometrics Conference, and workshops at Queen's University and the University of Toronto. We also thank the Social Sciences and Humanities Research Council of Canada for financial support. Any errors are our own.


This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time-varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time-series dynamics of jump clustering.