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dc.contributor.authorJayawardena, Nirodha Imali
dc.contributor.authorTodorova, Neda
dc.contributor.authorLi, Bin
dc.contributor.authorSu, Jen-Je
dc.date.accessioned2018-08-17T01:33:14Z
dc.date.available2018-08-17T01:33:14Z
dc.date.issued2016
dc.identifier.issn1873-6122en_US
dc.identifier.doi10.1016/j.econmod.2015.10.004en_US
dc.identifier.urihttp://hdl.handle.net/10072/99910
dc.description.abstractSince markets generally do not trade during overnight period, volatility cannot be estimated on a high-frequency basis. We adopt a new forecasting approach by using squared overnight return, pre-open volatility of the same asset and realized volatilities of related assets from other markets, where intraday data is still available while the Australian Stock Exchange (ASX) is closed, to predict stock volatility. We use a number of different specifications of the Heterogeneous Autoregressive (HAR) model to identify an optimal way to incorporate this additional information. We evaluate the forecasting performance of 45 ASX 200 stocks, categorized in three groups based on their annual total trading volumes, three Global Industry Classification Standard (GICS) indices and the S&P/ASX 200 index using a rolling estimation method. Our empirical analysis of the ASX constituents confirms the usefulness of using pre-open volatility of the same asset and realized volatilities of related assets from other markets when the ASX is closed for forecasting future volatility. Furthermore, we find that the predictive power of overnight information for all stocks and indices is higher during the market opening period and declines gradually over the trading day. However, the decrement is steeper for active stocks, suggesting that the predictive power is higher for inactively traded stocks. Finally, we evaluate the economic significance of the augmented HAR model that includes realized volatilities of related assets from other markets, and we find that it provides significant utility gains to a typical mean-variance investor.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofpagefrom592en_US
dc.relation.ispartofpageto608en_US
dc.relation.ispartofissuePart Ben_US
dc.relation.ispartofjournalEconomic Modellingen_US
dc.relation.ispartofvolume52en_US
dc.subject.fieldofresearchApplied Economics not elsewhere classifieden_US
dc.subject.fieldofresearchcode140299en_US
dc.titleForecasting stock volatility using after-hour information: Evidence from the Australian Stock Exchangeen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Business School, Department of Accounting, Finance and Economicsen_US
gro.hasfulltextNo Full Text


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