Abstract
This paper proposes a variant of a threshold stochastic conditional duration (TSCD) model for financial data at the transaction level. It assumes that the innovations of the duration process follow a threshold distribution with a positive support. In addition, it also assumes that the latent first-order autoregressive process of the log conditional durations switches between two regimes. The regimes are determined by the levels of the observed durations and the TSCD model is specified to be self-excited. A novel Markov-Chain Monte Carlo method (MCMC) is developed for parameter estimation of the model. For model discrimination, we employ deviance information criteria, which does not depend on the number of model parameters directly. Duration forecasting is constructed by using an auxiliary particle filter based on the fitted models. Simulation studies demonstrate that the proposed TSCD model and MCMC method work well in terms of parameter estimation and duration forecasting. Lastly, the proposed model and method are applied to two classic data sets that have been studied in the literature, namely IBM and Boeing transaction data.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.