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

Author

Listed:
  • Anthony Tay

    () (School of Economics, Singapore Management University)

  • 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)

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.

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," Working Papers 13-2007, Singapore Management University, School of Economics.
  • Handle: RePEc:siu:wpaper:13-2007
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    File URL: https://mercury.smu.edu.sg/rsrchpubupload/16348/PIN_JFE_submit.pdf
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    More about this item

    Keywords

    Autoregressive Conditional Duration; Market Microstructure; Probability of informed Trading; Transaction Data; Weibull Distribution;

    JEL classification:

    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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