A generalized bivariate mixture model for stock price volatility and trading volume

In the standard bivariate mixture model, the number of information arrivals which is typically modeled as a serially correlated random variable, determines the dynamics of stock price volatility and trading volume. An important limitation of this model is the assumption that the traders’ sensitivity to new information is constant over time, implying that every piece of information is treated alike. In this paper, I allow the latent number of information arrivals as well as the latent sensitivity to new information to be serially correlated random variables each endowed with their own dynamic behavior. In the resulting generalized mixture model, the behavior of volatility and volume results from the simultaneous interaction of the number of information arrivals and traders’ sensitivity to new information. The empirical results based on daily data for the IBM and Kodak stock reveals that the generalized mixture model improves the explanation of the behavior of volatility relative to the standard model. Furthermore, the short-run volatility dynamics are directed by the information arrival process, whereas the long-run dynamics are associated with the sensitivity to new information. Finally, the variation of the sensitivity to news is largely irrelevant for the behavior of trading volume which is mainly determined by the variation of the number of information arrivals.