Stock Market Volatility Dynamics: A Volume Filtered-GARCH Model

59 Pages Posted: 7 Mar 2016 Last revised: 8 Jun 2016

See all articles by Maxime Bonelli

Maxime Bonelli

HEC Paris - Finance Department

Date Written: June 7, 2016

Abstract

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.

Keywords: Mixture of Distribution, stochastic volatility, GARCH, trading volume

JEL Classification: C51, C58, G1, G12

Suggested Citation

Bonelli, Maxime, Stock Market Volatility Dynamics: A Volume Filtered-GARCH Model (June 7, 2016). Available at SSRN: https://ssrn.com/abstract=2743170 or http://dx.doi.org/10.2139/ssrn.2743170

Maxime Bonelli (Contact Author)

HEC Paris - Finance Department ( email )

France

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