Document Type
Other
Publisher
Edith Cowan University
Place of Publication
Perth, Western Australia
School
School of Accounting, Finance and Economics
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.
Comments
Ng, K., Allen, A., & Peiris, S. (2009). Fitting Weibull ACD Models to High Frequency Transactions Data : a semi-parametric approach based on estimating functions. Perth, Australia: Edith Cowan University.