Bayesian analysis of the stochastic conditional duration model
Copyright policy )Bayesian analysis of the stochastic conditional duration model
A Bayesian Markov Chain Monte Carlo methodology is developed for estimating the stochastic conditional duration model. The conditional mean of durations between trades is modelled as a latent stochastic process, with the conditional distribution of durations having positive support. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms, with the latent vector sampled in blocks. The suggested approach is shown to be preferable to the quasi-maximum likelihood approach, and its mixing speed faster than that of an alternative single-move algorithm. The methodology is illustrated with an application to Australian intraday stock market data.
- Queensland University of Technology Australia
- Monash University Australia
Applications of statistics to actuarial sciences and financial mathematics, Bayesian inference, non-Gaussian state space model, Kalman filter and simulation smoother, latent variable model, Latent Variable Model, Inference from stochastic processes and prediction, Transaction Data, Markov chain Monte Carlo, Time series, auto-correlation, regression, etc. in statistics (GARCH), Transaction data, Latent factor model, Non-Gaussian state space model, Kalman filter and simulation smoother., Markov Chain Monte Carlo, transaction data, Non-Gaussian State Space Model, Kalman Filter and Simulation Smoother, Uncategorized, jel: jel:C41, jel: jel:C11, jel: jel:C15
Applications of statistics to actuarial sciences and financial mathematics, Bayesian inference, non-Gaussian state space model, Kalman filter and simulation smoother, latent variable model, Latent Variable Model, Inference from stochastic processes and prediction, Transaction Data, Markov chain Monte Carlo, Time series, auto-correlation, regression, etc. in statistics (GARCH), Transaction data, Latent factor model, Non-Gaussian state space model, Kalman filter and simulation smoother., Markov Chain Monte Carlo, transaction data, Non-Gaussian State Space Model, Kalman Filter and Simulation Smoother, Uncategorized, jel: jel:C41, jel: jel:C11, jel: jel:C15
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