features and, most importantly, by temporal dependences in the information arrival
process, these models provide an explanation of how such temporal dependence occurs.
GARCH-type empirical models do not, however, provide a theoretical explanation of
why volatility persists or if any, what the exact impact of information flow is on
volatility. An appealing answer to these questions could be inferred from the mixture
of distribution hypothesis (MDH), which argues that the variance of returns at a given
interval is proportional to the rate of information arrival on the market (Clark, 1973;
Epps and Epps, 1976; Tauchen and Pitts, 1983; Harris, 1987; Andersen, 1996;
Liesenfeld, 1998, 2001; Darolles, Le Fol, and Mero, 2009, among others). The phenom-
enon of volatility clustering could then be seen as a reflection of the serial correlation of
information arrival frequencies (Lamoureux and Lastrapes, 1990).
This chapter considers the effect of the rate of information arrival on uncertainty
(return volatility), where information arrival is proxied by the number of firm-specific
announcements per given interval. Prior research (Kalev et al., 2004) has shown that
both the quantity and quality of news are superior proxies for information flow. The
current chapter builds on and further extends the Kalev et al. (2004) empirical frame-
work. Similar to Kalev et al. (2004), we proxy the rate of information arrival with the
number of firm-specific news announcements. We differ from Kalev et al. (2004) by
examining the impact of firm-specific news announcements on the price volatility of the
S&P/ASX 200 Index as well as futures contracts on the S&P/ASX 200 Index (the SPI
200 Futures). More specifically, we ask the following questions: To what extent is
volatility clustering a reflection of the serial correlation of information arrival fre-
quencies? Do firm-specific news announcements—a proxy for the intensity of the rate
of information arrivals—capture stock price index volatility persistence better than the
rate of information flow as proxied by trading volume? Do volatility and the volatility
persistence of futures on the index differ from spot price index volatility and its
persistence? After accounting for the rate of information flow, does the reduction in
volatility persistence alter or remain unchanged in times of severe financial crisis, such as
the Global Financial Crisis (GFC)?
We proceed as follows. Section 12.2 provides an overview of prior literature on the
news arrival–volatility relation, with a special focus on the MDH. Section 12.3 describes
the data used in the chapter, and Section 12.4 discusses results and implications. Section
12.5 concludes the chapter, and Section 12.A provides a technical appendix (see p. 283)
which explains the research methodology employed in the chapter.
12.2 Background literature
In his seminal work, Clark (1973) proposed that a mixture of normal distributions
should be utilized to model the empirical distribution of security price changes. Clark’s
model assumes that news events are important for pricing securities and that news
arrives at a random rate over the trading period. This is normally referred to in the
literature as the Mixture of Distribution Hypothesis (MDH). Using the same assump-
tions, Tauchen and Pitt (1983) and Harris (1986, 1987) show that the joint distribution
of trading volume and price changes can be modeled by a mixture of bivariate normal
distributions. More specifically, in the standard MDH model, the daily price change and
trading volume are the sum of independent intraday price changes and volume that
272 News and risk