Publication Type
Working Paper
Version
publishedVersion
Publication Date
1-2007
Abstract
This paper implements the Asymmetric AutoregressiveConditional Duration (AACD) model of Bauwens and Giot (2003) to analyzeirregularly spaced transaction data of trade direction, namely buy versus sellorders. We examine the influence of lagged transaction duration, lagged volumeand lagged trade direction on transaction duration and direction. Our resultsare applied to estimate the probability of informed trading (PIN) based on theEasley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley-Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sellorders, the AACD model makes full use of transaction data and allows forinteractions between buy and sell orders.
Keywords
Autoregressive Conditional Duration, Market Microstructure, Probability of Informed Trading, Transaction Data, Weibull Distribution
Discipline
Econometrics | Finance | Finance and Financial Management
Research Areas
Econometrics
First Page
1
Last Page
22
Publisher
SMU Economics and Statistics Working Paper Series, No. 13-2007
City or Country
Singapore
Citation
TAY, Anthony S.; TING, Christopher; TSE, Yiu Kuen; and WARACHKA, Mitch.
Modeling transaction data of trade direction and estimation of probability of informed trading. (2007). 1-22.
Available at: https://ink.library.smu.edu.sg/soe_research/1899
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Comments
Published in Journal of Financial Econometrics, Volume 7, Issue 3, 2009, Pages 288–311, https://doi.org/10.1093/jjfinec/nbp005