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.
Autoregressive Conditional Duration, Market Microstructure, Informed Trading
Econometrics | Finance | Finance and Financial Management
Singapore Management University School of Economics, Paper No 09-2004
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TAY, Anthony S.; TING, Christopher; TSE, Yiu Kuen; and Warachka, Mitchell.
Transaction-data analysis of marked durations and their implications for market microstructure. (2004). Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/2373
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