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
Abstract
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
Suggested Citation: Suggested Citation
Vrontos, Ioannis D. and Dellaportas, Petros and Politis, Dimitris, Full Bayesian Inference for GARCH and Egarch Models. Journal of Business and Economics Statistics, Vol. 18, No. 2, pp. 187-198, 2000, Available at SSRN: https://ssrn.com/abstract=609341
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