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

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

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 direc...

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Main Authors: TAY, Anthony S., TING, Christopher, TSE, Yiu Kuen, WARACHKA, Mitch
格式: text
語言:English
出版: Institutional Knowledge at Singapore Management University 2007
主題:
Autoregressive Conditional Duration
Market Microstructure
Probability of Informed Trading
Transaction Data
Weibull Distribution
Econometrics
Finance
Finance and Financial Management
在線閱讀:https://ink.library.smu.edu.sg/soe_research/1899
https://ink.library.smu.edu.sg/context/soe_research/article/2898/viewcontent/ModelingTransactionData.pdf
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機構: Singapore Management University
語言: English
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總結: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.

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