Empirical Research On The Autoregressive Conditional Duration Model Of Market Making Strategies In The Market Microstructure

The high-frequency micro-market transactions and the low transaction has different characteristics,the generalized autoregressive conditional heteroskedasticity model(GARCH)is not suitable for modelling irregularly spaced financial data.The autoregressive conditional duration model(ACD)is modeling to fit and predict the duration of the financial high-frequency transactions.The autoregressive conditional duration of financial high-frequency transactions combined with the generalized autoregressive conditional heteroskedasticity model,so-called the ACD-GARCH model solve the high frequency data in irregularly spaced trading problems via volatility modeling.In the article focus on the theoretical point of view on the definition of the microscopic structure of the market,and the impact of market mechanisms in futures markets,which followed by the high-frequency characteristics.Based on the time series analysis of financial data processing method analyze the nature of the durations,then the use of autoregressive conditional duration model conditioned on three commodities futures.In the results show that the duration of ACD-GARCH models in the futures markets microstructure of commodities more suitable for modeling and forecasting volatility.