Using high-frequency transaction data to estimate the probability of informed trading

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. Un...

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Bibliographic Details
Main Authors: TAY, Anthony S., TING, Christopher, TSE, Yiu Kuen, WARACHKA, Mitchell
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2009
Subjects:
autoregressive conditional duration
market microstructure
probability of informed trading
transaction data
Weibull distribution
Econometrics
Finance
Finance and Financial Management
Online Access:https://ink.library.smu.edu.sg/soe_research/519
https://ink.library.smu.edu.sg/context/soe_research/article/1518/viewcontent/UsingHighFreqTransactionData_2009_JFE.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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