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

Author

Listed:
  • 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.

Suggested Citation

  • Anthony Tay & Christopher Ting & Yiu Kuen Tse & Mitch Warachka, 2004. "Transaction-Data Analysis of Marked Durations and Their Implications for Market Microstructure," Working Papers 09-2004, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:09-2004
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    File URL: https://mercury.smu.edu.sg/rsrchpubupload/1850/MM_dist.pdf
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    Citations

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    Cited by:

    1. Kalaitzoglou, Iordanis Angelos & Ibrahim, Boulis Maher, 2015. "Liquidity and resolution of uncertainty in the European carbon futures market," International Review of Financial Analysis, Elsevier, vol. 37(C), pages 89-102.
    2. Chiranjit Dutta & Kara Karpman & Sumanta Basu & Nalini Ravishanker, 2023. "Review of Statistical Approaches for Modeling High-Frequency Trading Data," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 85(1), pages 1-48, May.
    3. Anthony Tay & Christopher Ting, 2006. "Intraday stock prices, volume, and duration: a nonparametric conditional density analysis," Empirical Economics, Springer, vol. 30(4), pages 827-842, January.
    4. Iordanis Angelos Kalaitzoglou & Boulis Maher Ibrahim, 2015. "Liquidity and resolution of uncertainty in the European carbon futures market," Post-Print hal-01107956, HAL.

    More about this item

    Keywords

    Autoregressive Conditional Duration; Market Microstructure; Informed Trading;
    All these keywords.

    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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