Full Bayesian Inference for GARCH and EGARCH Models

Ioannis Vrontos (), Petros Dellaportas and D N Politis

Journal of Business & Economic Statistics, 2000, vol. 18, issue 2, 187-98

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.

Date: 2000
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Persistent link: https://EconPapers.repec.org/RePEc:bes:jnlbes:v:18:y:2000:i:2:p:187-98

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