Fitting weibull ACD models to high frequency transactions data: A semi-parametric approach based on estimating functions

Ng, K.H. and Allen, D. and Peiris, S. (2009) Fitting weibull ACD models to high frequency transactions data: A semi-parametric approach based on estimating functions. In: 15th International Conference on Computing in Economics and Finance, 15-17 July 2009, Sydney, Australia. (Submitted)

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Weibull distribution, Autoregression, Conditional duration, Estimating function, Maximum likelihood, Standard error, Applications, Financial data, Semiparametric, High frequency data, Transactions, Time series
Subjects: A General Works > AC Collections. Series. Collected works
Depositing User: Mr. Mohd Samsul Ismail
Date Deposited: 18 Dec 2014 00:37
Last Modified: 18 Dec 2014 00:37
URI: http://eprints.um.edu.my/id/eprint/11167

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