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

Gael Margaret Martin, Chris Strickland and Catherine Forbes

No 324, Econometric Society 2004 Australasian Meetings from Econometric Society

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.

Keywords: Non Gaussian; Kalman Filter; Bayesian; Markov Chain Monte Carlo (search for similar items in EconPapers)
JEL-codes: C11 C15 C32 (search for similar items in EconPapers)
Date: 2004-08-11

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