Artikel
Threshold stochastic conditional duration model for financial transaction data
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Journal of Risk and Financial Management ; ISSN: 1911-8074 ; Volume: 12 ; Year: 2019 ; Issue: 2 ; Pages: 1-21 ; Basel: MDPI
- Classification
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Wirtschaft
Econometric and Statistical Methods and Methodology: General
Duration Analysis; Optimal Timing Strategies
General Financial Markets: General (includes Measurement and Data)
- Subject
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stochastic conditional duration
threshold
Bayesian inference
Markov-Chain Monte Carlo
probability integral transform
deviance information criterion
- Event
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Geistige Schöpfung
- (who)
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Men, Zhongxian
Kolkiewicz, Adam W.
Wirjanto, Tony S.
- Event
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Veröffentlichung
- (who)
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MDPI
- (where)
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Basel
- (when)
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2019
- DOI
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doi:10.3390/jrfm12020088
- Handle
- Last update
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10.03.2025, 11:43 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Men, Zhongxian
- Kolkiewicz, Adam W.
- Wirjanto, Tony S.
- MDPI
Time of origin
- 2019
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