Publication Type
Presentation
Version
acceptedVersion
Publication Date
2-2009
Abstract
This paper applies the asymmetric autoregressive conditional duration (AACD) model of Bauwens and Giot (2003) to estimate the probability of informed trading (PIN) using irregularly spaced transaction data. We model trade direction (buy versus sell orders) and the duration between trades jointly. Unlike the Easley, Hvidkjaer, and O'Hara(2002) approach, which uses the aggregate numbers of daily buy and sell orders to estimate PIN, our methodology allows for interactions between consecutive buy-sell orders and accounts for the duration between trades and the volume of trade. We extend the Easley-Hvidkjaer-O'Hara framework by allowing the probabilities of good news and bad news to vary each day. Our PIN estimates can be computed daily as well as over intraday intervals.
Discipline
Finance and Financial Management | Portfolio and Security Analysis
Research Areas
Finance; Econometrics
Publication
Singapore Conference on Quantitative Finance
First Page
1
Last Page
27
City or Country
Singapore
Citation
Tay, Anthony S.; Ting, Christopher; TSE, Yiu Kuen; and WARACHKA, Mitchell Craig.
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading. (2009). Singapore Conference on Quantitative Finance. 1-27.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/1900
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://ssrn.com/abstract=1425981