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Transaction-Data Analysis of Marked Durations and Their Implications for Market Microstructure

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Author Info

  • Anthony Tay

    () (School of Economics and Social Sciences, Singapore Management University)

  • Christopher Ting

    () (Lee Kong Chian School of Business, Singapore Management University)

  • Yiu Kuen Tse

    () (School of Economics and Social Sciences, Singapore Management University)

  • Mitch Warachka

    () (Lee Kong Chian School of Business, Singapore Management University)

Abstract

We propose an Autoregressive Conditional Marked Duration (ACMD) model for the analysis of irregularly spaced transaction data. Based on the Autoregressive Conditional Duration (ACD) model, the ACMD model assigns marks to characterize events such as tick movements and trade directions (buy/sell). Applying the ACMD model to tick movements, we study the influence of trade frequency, direction and size on price dynamics, volatility and the permanent and transitory price impacts of trade. We also apply the ACMD model to analyze trade-direction data and estimate the probability of informed trading (PIN). We find that trade frequency has a critical role in price dynamics while the contribution of volume to price impacts, volatility, and the probability of informed trading is marginal.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 09-2004.

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Length: 52 pages
Date of creation: Mar 2004
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:09-2004

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Related research

Keywords: Autoregressive Conditional Duration; Market Microstructure; Informed Trading;

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