Transaction-Data Analysis of Marked Durations and Their Implications for Market Microstructure
AbstractWe 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 InfoPaper provided by Singapore Management University, School of Economics in its series Working Papers with number 09-2004.
Length: 52 pages
Date of creation: Mar 2004
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Find related papers by 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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- 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.
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