Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading
This paper implements the Asymmetric Autoregressive Conditional Duration (AACD) model of Bauwens and Giot (2003) to analyze irregularly spaced transaction data of trade direction, namely buy versus sell orders. We examine the influence of lagged transaction duration, lagged volume and lagged trade direction on transaction duration and direction. Our results are applied to estimate the probability of informed trading (PIN) based on the Easley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley- Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sell orders, the AACD model makes full use of transaction data and allows for interactions between buy and sell orders.
|Date of creation:||Jan 2007|
|Publication status:||Published in SMU Economics and Statistics Working Paper Series|
|Contact details of provider:|| Postal: 90 Stamford Road, Singapore 178903|
Phone: 65-6828 0832
Fax: 65-6828 0833
Web page: http://www.economics.smu.edu.sg/
More information through EDIRC
|Order Information:|| Email: |
When requesting a correction, please mention this item's handle: RePEc:siu:wpaper:13-2007. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (QL THor)
If references are entirely missing, you can add them using this form.