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

See all articles by Ioannis D. Vrontos

Ioannis D. Vrontos

Athens University of Economics and Business

Petros Dellaportas

Athens University of Economics and Business

Dimitris N. Politis

University of California, San Diego (UCSD) - Department of Mathematics

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

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

Ioannis D. Vrontos (Contact Author)

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Petros Dellaportas

Athens University of Economics and Business ( email )

76 Patission Street
Athens, 104 34
Greece

Dimitris Politis

University of California, San Diego (UCSD) - Department of Mathematics ( email )

9500 Gilman Drive
La Jolla, CA 92093-0112
United States
858-534-5861 (Phone)
858-534-5273 (Fax)

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