Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading

Anthony S Tay (), Christopher Ting (), Yiu Kuen Tse () and Mitch Warachka ()
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Christopher Ting: Lee Kong Chian School of Business, Singapore Management University
Yiu Kuen Tse: School of Economics, Singapore Management University
Mitch Warachka: Lee Kong Chian School of Business, Singapore Management University

No 13-2007, Working Papers from Singapore Management University, School of Economics

Abstract: This paper implements the Asymmetric Autoregressive Conditional Duration (AACD) model of Bauwens and Giot (2003) to analyze irregularly spaced transaction data of trade direction, namely buy versus sell orders. We examine the influence of lagged transaction duration, lagged volume and lagged trade direction on transaction duration and direction. Our results are applied to estimate the probability of informed trading (PIN) based on the Easley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley- Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sell orders, the AACD model makes full use of transaction data and allows for interactions between buy and sell orders.

Keywords: Autoregressive Conditional Duration; Market Microstructure; Probability of informed Trading; Transaction Data; Weibull Distribution (search for similar items in EconPapers)
JEL-codes: C41 G12 (search for similar items in EconPapers)
Date: 2007-01
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Published in SMU Economics and Statistics Working Paper Series

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