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

Author

Listed:
  • Anthony Tay

    (SMU)

  • Christopher Ting
  • Yiu Kuen Tse
  • Mitch Warachka

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 OHara (2002) framework. Unlike the Easley- Hvidkjaer-OHara 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.

Suggested Citation

  • Anthony Tay & Christopher Ting & Yiu Kuen Tse & Mitch Warachka, 2007. "Modeling Transaction Data of Trade Direction and Estimation of Probability of Informed Trading," Finance Working Papers 22483, East Asian Bureau of Economic Research.
    • Handle: RePEc:eab:financ:22483
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    References listed on IDEAS

    as
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