An Examination of the Role of Time in Ultra-High Frequency Data and its Impact on Price Revisions in News Corporation Stock
David E. Allen
University of South Australia; School of Mathematics and Statistics, The University of Sydney; Financial Research Network (FIRN)
University of Sydney - School of Mathematics and Statistics
Wenling Joey Yang
Securities Industry Research Centre of Asia Pacific (SIRCA); Edith Cowan University - School of Finance and Business Economics
Edith Cowan University Accounting, Finance and Economics Working Paper
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, C22working papers series
Date posted: April 9, 2004
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