Modeling transaction data of trade direction and estimation of probability of informed trading

• Main Authors: TAY, Anthony S., TING, Christopher, TSE, Yiu Kuen, WARACHKA, Mitch
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2007
• 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/1899; https://ink.library.smu.edu.sg/context/soe_research/article/2898/viewcontent/ModelingTransactionData.pdf
• Institution: Singapore Management University

This paper implements the Asymmetric AutoregressiveConditional Duration (AACD) model of Bauwens and Giot (2003) to analyzeirregularly spaced transaction data of trade direction, namely buy versus sellorders. We examine the influence of lagged transaction duration, lagged volumeand lagged trade direction on transaction duration and direction. Our resultsare applied to estimate the probability of informed trading (PIN) based on theEasley, Hvidkjaer and O’Hara (2002) framework. Unlike the Easley-Hvidkjaer-O’Hara model, which uses the daily aggregate number of buy and sellorders, the AACD model makes full use of transaction data and allows forinteractions between buy and sell orders.