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Bayesian Estimation of Non-Gausian Time Series with Applicaitons to Transaction Data

Author

Listed:
  • Gael Martin
  • Chris Strickland
  • Catherine Forbes

Abstract

A general Bayesian Markov Chain Monte Carlo methodology is utilized for conducting an analysis of the intensity process of stock market data. The sampling scheme employed is a hybrid of the Gibbs and Metropolis Hastings algorithms. Both duration and count data time series approaches are utilized to model trading intensity. Regression effects are incorporated in the model so that market microstructure hypothesis can be tested. The specific analysis is undertaken on Australian stock market data.

Suggested Citation

  • Gael Martin & Chris Strickland & Catherine Forbes, 2004. "Bayesian Estimation of Non-Gausian Time Series with Applicaitons to Transaction Data," Econometric Society 2004 Australasian Meetings 324, Econometric Society.
    • Handle: RePEc:ecm:ausm04:324
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    More about this item

    Keywords

    Non Gaussian; Kalman Filter; Bayesian; Markov Chain Monte Carlo;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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