Modeling the Intraday Return Volatility Process in the Australian Equity Market: An Examination of the Role of Information Arrival in S&amp;P/ASX 50 Stocks

Andrew C Worthington,Helen Higgs

doi:10.5539/ibr.v1n2p87

Abstract

This paper examines the intraday return volatility process in Australian company stocks. The data set employed consists of five-minute returns, trading volumes and bid-ask spreads over the period 31 December 2002 to 4 March 2003 for the fifty national and multinational stocks comprising the S&P/ASX 50 index. The GARCH and asymmetric GARCH namely Threshold ARCH (TARCH) processes are used to model the time-varying variance in the intraday return series and the inclusion of news arrival as proxied by the contemporaneous and lagged volume of trade and bid-ask spread together with day-of-week effects are used as exogenous explanatory variables. The results indicate strong persistence in volatility for the fifty stocks even with the day-of-week effects and contemporaneous and lagged volume of trade and bid-ask spread included as explanatory variables in the models. Overall, while there is much variation among the stocks included in terms of the role of the irregular arrival of new information in generating GARCH effects and the degree of persistence, all of the volatility processes are mean reverting.

Highlights

The bulk of volatility modeling has been concerned with univariate characteristics such that the volatility of a return series is related only to information in its own history
The distributional properties of Australian company intraday stock returns indicates that generalized autoregressive conditional heteroskedastistic (GARCH) models can be used to examine the dynamics of the return volatility process
With the inclusion of the contemporaneous bid-ask spread in the Threshold autoregressive conditional heteroskedasticity (ARCH) (TARCH)(1,1) model, the results show that the GARCH effects remain strongly significant with the exception of GPT

Summary

Introduction

It goes without saying that knowledge of stock return volatility is important. In any number of asset pricing and portfolio management problems this knowledge, as encapsulated in volatility models, is used to make predictions that help market actors make better financial decisions. The autoregressive conditional heteroskedasticity (ARCH) model and its various extensions has been shown to provide a good fit for many financial return series where an autoregressive structure is imposed on the conditional variance These allow the volatility shocks to persist over time and to revert to that more normal level. The overall hypothesis is to assess return and volatility and relationships in fifty Australian stocks by incorporating new arrival of information namely, the inclusion of news arrival as proxied by the contemporaneous and lagged volume of trade, bid-ask spread together with day-of-week and information asymmetry effects are used as exogenous explanatory variables These allow the estimation of volatility clustering over time, and to determine whether shocks persist over time, and/or revert to a more normal level.

Data and summary statistics

Model specificationN

Empirical results

Findings

Concluding remarks