occur as a result of the arrival of information events. After each event, price changes as a
result of traders responding to the new information. Intraday price changes and the
volume of those trades are generally assumed to be jointly independent and identically
distributed with finite variance. If the number of news event arrivals are sufficiently large
for a given interval then, following the Central Limit Theorem, the joint distribution of
price changes and trading volume are approximately bivariate-normal and conditional
on the number of information events. Thus, the conditional variance of price changes is
an increasing function of the rate of information flow on the market.
Building on the MDH, Lamoureux and Lastrapes (1990) argue that persistence in the
conditional variance of price changes reflects the time-series properties (i.e., serial
correlation) in the news arrival process. Therefore, when the impact of information
flow is accounted for, most observed volatility persistence should disappear. Using
contemporaneous volume as a proxy for information arrivals, Lamoureux and
Lastrapes (1990) document that volatility persistence disappears when volume is
included in the variance equation of the GARCH model. Extending Lamoureux and
Lastrapes’ (1990) work by using the ‘‘surprised’’ component of volume, Wagner and
Marsh (2005) document that the surprised volume helps explain volatility persistence as
well as excess kurtosis in seven major equity markets.
The use of contemporaneous volume as a proxy for information arrivals, however,
introduces various potential issues. First, trading volume cannot be assumed to be
exogenous, as both volatility and trading volume are simultaneously influenced by
the latent information arrival process (see, among others, Tauchen and Pitts, 1983;
Harris, 1987; Foster and Viswanathan, 1993, 1995; He and Wang, 1995). Second,
volume can be driven by liquidity traders (Kyle, 1985), noise traders (DeLong et al.,
1990; Campbell and Kyle, 1993), by difference in opinion or by interpretation of news
among traders even without information arrival (Grundy and McNichols, 1989; Kim
and Verrecchia, 1991). In particular, Andersen (1996) documents that 34% to 75% of
daily trading volume is unrelated to news arrival. Third, volume may even lag behind
information flow when the information is private (He and Wang, 1995). Finally, in
strategic microstructure models with asymmetric information (see, among others, Kyle,
1985; Admati and Pfleiderer, 1988), informed traders may break their large orders into
several small- to medium-sized trades to exploit their informational advantage.^2 This
strategic trading could further attenuate the positive relation between trading volume
and information arrival.
Ederington and Lee (1993) provide an alternative proxy for the rate of information
arrivals (i.e., macroeconomic news announcements). They conclude that the arrivals
of macroeconomic news announcements explain the intraday pattern of volatility in
the interest rate and foreign exchange futures markets. Using news headlines appearing
on the Reuters Money News Alert as a proxy for public information releases,
DeGennaro and Shrieves (1997) find that news releases are an important determinant
for volatility. Investigating the mark/dollar and the yen/dollar, Melvin and Yin (2000)
also document that public information arrivals, as reflected by the number of news
headlines reported on the Reuters Money Market Headlines News, have a positive effect
on volatility.
Firm-specific news arrival and the volatility of intraday stock index and futures returns 273(^2) For empirical evidence regarding this type of strategic trading known in the literature as stealth trading, see Barclay and
Warner (1993).