Document Type
Conference Item
Publication Date
7-1-2009
Abstract
Autoregressive conditional duration (ACD) models play an important role in financial modeling. This paper considers the estimation of the Weibull ACD model using a semiparametric approach based on the theory of estimating functions (EF). We apply the EF and the maximum likelihood (ML) methods to a data set given in Tsay (2003, p203) to compare these two methods. It is shown that the EF approach is easier to apply in practice and gives better estimates than the MLE. Results show that the EF approach is compatible with the ML method in parameter estimation. Furthermore, the computation speed for the EF approach is much faster than for the MLE and therefore offers a significant reduction of the completion time.
Keywords
Weibull distribution, Autoregression, Conditional duration, Estimating function, Maximum likelihood, Standard error, Applications, Financial data, Semiparametric, High frequency data, Transactions, Time series
Event Title
15th International Conference on Computing in Economics and Finance
Event Location
Sydney, Australia
Event Dates
15-17 July 2009
Event Type
conference