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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sg-smu-ink.lkcsb_research-28992018-07-09T07:36:17Z Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading Tay, Anthony S. Ting, Christopher TSE, Yiu Kuen WARACHKA, Mitchell Craig 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. 2009-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/1900 https://ink.library.smu.edu.sg/context/lkcsb_research/article/2899/viewcontent/tseyiukuen_HighFreqTransDataInformedTrading.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University Finance and Financial Management Portfolio and Security Analysis |
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Finance and Financial Management Portfolio and Security Analysis Tay, Anthony S. Ting, Christopher TSE, Yiu Kuen WARACHKA, Mitchell Craig Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
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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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text |
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Tay, Anthony S. Ting, Christopher TSE, Yiu Kuen WARACHKA, Mitchell Craig |
author_facet |
Tay, Anthony S. Ting, Christopher TSE, Yiu Kuen WARACHKA, Mitchell Craig |
author_sort |
Tay, Anthony S. |
title |
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
title_short |
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
title_full |
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
title_fullStr |
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
title_full_unstemmed |
Using High-Frequency Transaction Data to Estimate the Probability of Informed Trading |
title_sort |
using high-frequency transaction data to estimate the probability of informed trading |
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Institutional Knowledge at Singapore Management University |
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2009 |
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https://ink.library.smu.edu.sg/lkcsb_research/1900 https://ink.library.smu.edu.sg/context/lkcsb_research/article/2899/viewcontent/tseyiukuen_HighFreqTransDataInformedTrading.pdf |
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