Publication Type
Working Paper
Version
publishedVersion
Publication Date
3-2004
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.
Keywords
Autoregressive Conditional Duration, Market Microstructure, Informed Trading
Discipline
Econometrics | Finance | Finance and Financial Management
Research Areas
Finance; Econometrics
First Page
1
Last Page
51
Publisher
SMU Economics and Statistics Working Paper, No. 09-2004
City or Country
Singapore
Citation
TAY, Anthony S.; TING, Christopher; TSE, Yiu Kuen; and Warachka, Mitchell.
Transaction-data analysis of marked durations and their implications for market microstructure. (2004). 1-51.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/2373
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.