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

  • John M. Maheu

    (Department of Economics, University of Toronto)

  • Thomas H. McCurdy

    (Joseph L. Rotman School of Management, University of Toronto)

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. Copyright 2004 by The American Finance Association.

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Article provided by American Finance Association in its journal The Journal of Finance.

Volume (Year): 59 (2004)
Issue (Month): 2 (04)
Pages: 755-793

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Handle: RePEc:bla:jfinan:v:59:y:2004:i:2:p:755-793
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