Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading
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.
autoregressive conditional duration, market microstructure, probability of informed trading, transaction data, Weibull distribution
Finance and Financial Management | Portfolio and Security Analysis
Journal of Financial Econometrics
Oxford University Press
City or Country
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). Journal of Financial Econometrics. 7, (3), 288-311. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/1901