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

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Author Info
Gael Martin
Chris Strickland
Catherine Forbes

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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.

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Publisher Info
Paper provided by Econometric Society in its series Econometric Society 2004 Australasian Meetings with number 324.

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Date of creation: 11 Aug 2004
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Handle: RePEc:ecm:ausm04:324

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Related research
Keywords: Non Gaussian; Kalman Filter; Bayesian; Markov Chain Monte Carlo;

Find related papers by JEL classification:
C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Bayesian Analysis
C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: General - - - Statistical Simulation Methods
C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions

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This page was last updated on 2009-11-6.

This information is provided to you by IDEAS at the Department of Economics, College of Liberal Arts and Sciences, University of Connecticut using RePEc data on a server sponsored by the Society for Economic Dynamics.