Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading

Publication Type

Journal Article

Publication Date

5-2009

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.

Keywords

autoregressive conditional duration, market microstructure, probability of informed trading, transaction data, Weibull distribution

Discipline

Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Finance; Econometrics

Publication

Journal of Financial Econometrics

Volume

7

Issue

3

First Page

288

Last Page

311

ISSN

1479-8409

Identifier

10.1093/jjfinec/nbp005

Publisher

Oxford University Press

City or Country

London, UK

Additional URL

https://doi.org/10.1093/jjfinec/nbp005

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