The Handbook of News Analytics in Finance Edited by L. Mitra and G. Mitra
#2011 John Wiley & Sons
Petko S. Kalev and Huu Nhan Duong
ABSTRACT
This chapter investigates the impact of the rate of information arrival on return
volatility. Prior research (Kalev et al., 2004) has shown that both the quantity and
quality of news are superior proxies for information flow. In the current chapter we
utilize high-frequency data based on the S&P/ASX 200 Index as well as futures contracts
on the S&P/ASX 200 Index over the period from October 2003 to September 2009.
Volatility persistence appears to be significantly reduced by inclusion of the number of
specific news items into the variance equations of the spot and futures index. Overall,
our findings are consistent with the mixture of distribution hypothesis (MDH).
12.1 INTRODUCTION
The relation (interrelation) between the flow of information and market uncertainty has
been an important topic for research over the last half of the 20th century. More
specifically, the dynamics between the flow of information and market uncertainty
has been the key factor that impacts security price formation, price discovery, market
participant behavior (price reaction or overreaction), and overall market stability.
Nowadays, researchers agree that variation in the frequency of information arrivals
drives volatility and volatility clustering of security prices (Ross, 1989; Jones, Kaul, and
Lipson, 1994; Ane ́ and Geman, 2000). High-frequency data availability and recent
advances in modeling of heteroskedastic time-series data enable empirical researchers
to address the most puzzling and intriguing feature of price volatility; namely, its strong
persistence (Goodhart and O’Hara, 1997).^1 Goodhart and O’Hara (1997) point out that,
since the Generalized Autoregressive Conditional Heteroskedasticity ((G)ARCH) pro-
cesses of Engle (1982) and Bollerslev (1986) are naturally motivated by time-varying
12
Firm-specific news arrival and the volatility
of intraday stock index and
futures returns
(^1) For a comprehensive review of ARCH/GARCH-type modeling, see Bollerslev, Chou, and Kroner (1992). Engle (2002)
defines some new frontiers for ARCH models. Refer to Bauwens, Laurent, and Rombouts (2006) and Asai, McAleer, and Yu
(2006) among others for surveys on multivariate GARCH and multivariate stochastic volatility specifications, respectively.