Stock Market Volatility Dynamics : A Volume Filtered-GARCH Model

We present a two-factor volatility model to study the impact of news arrival and trading volume on stock returns variance. The model can explicitly account for the association between volatility and volume, as well as the persistence in equity variance. Unlike the standard "Mixture of Distribution Hypothesis", the conditional variance is governed by the stochastic information arrival and adds a persistent GARCH component, in order to disentangle transient from persistent volatility variations. The common observation that large volumes are associated with high volatility is explained by the fact that unexpected shocks in volume increase volatility, which is not the case for expected volumes of trading. Furthermore, the persistence of volatility is essentially unrelated to volume implying that the latter does not explain ARCH effect. Finally, we find that unexpected shocks in volume and the persistent GARCH component are both main drivers of volatility dynamics