Full Bayesian Inference for GARCH and Egarch Models
Journal of Business and Economics Statistics, Vol. 18, No. 2, pp. 187-198, 2000
Posted: 26 Oct 2004
A full Bayesian analysis of GARCH and EGARCH models is proposed consisting of parameter estimation, model selection and volatility prediction. The Bayesian paradigm is implemented via Markov-chain Monte Carlo methodologies. We provide implementation details and illustrations using the General index of the Athens stock exchange.
Keywords: Markov-chain Monte Carlo, model averaging, reversible jump, volatility prediction
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