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An Examination of the Role of Time in Ultra-High Frequency Data and its Impact on Price Revisions in News Corporation StockDavid E. AllenEdith Cowan University - School of Finance and Business Economics; Financial Research Network (FIRN) Shelton PeirisUniversity of Sydney - School of Mathematics and Statistics Wenling Joey YangSecurities Industry Research Centre of Asia Pacific (SIRCA); Edith Cowan University - School of Finance and Business Economics December 2003 Edith Cowan University Accounting, Finance and Economics Working Paper Abstract: We consider a new class of time series models (introduced by Engle and Russell (1998)) used in statistical applications in finance. These models treat the time between events (durations) as a stochastic process and the corresponding durations are modelled using a theory similar to that of autoregressive processes. This new class of time series models is called Autoregressive Conditional Duration (ACD) models. We apply the theory to analyse the behaviour of an Australian Stock: News Corporation, using a high-frequency data set obtained from SIRCA.
Number of Pages in PDF File: 23 Keywords: Autoregressive, Conditional expectation, Intensity, Hazard function JEL Classification: G12, C22 working papers seriesDate posted: April 9, 2004Suggested CitationContact Information
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