ACD–Modeling Irregular Spaced Transaction Data
The following document contains a brief summary of the paper titled, “Autoregressive Conditional Duration - A New Model for Irregularly Spaced Transaction Data” by Engle and Russell.
The paper models the duration between transactions.With the ease of availability of HF data, there needs to be a model that captures irregularly spaced timestamps. It is obvious that neither a standard Poisson process nor a non-homogeneous Poisson process is going to be a good fit. The conditional intensity function has to depend on the past history of transaction times. The authors formulate a particular form for the conditional intensity function and explain the clustering phenomenon as well as some well known market microstructure patterns.