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Efficient Bayesian Estimation and Combination of GARCH-Type Models

David Ardia and Lennart F. Hoogerheide
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Lennart F. Hoogerheide: Erasmus University Rotterdam

No 10-046/4, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: This discussion paper resulted in a chapter in: (K. Bocker (Ed.)) 'Rethinking Risk Measurement and Reporting - Volume II: Examples and Applications from Finance', 2010, London: Riskbooks. This paper proposes an up-to-date review of estimation strategies available for the Bayesian inference of GARCH-type models. The emphasis is put on a novel efficient procedure named AdMitIS. The methodology automatically constructs a mixture of Student-t distributions as an approximation to the posterior density of the model parameters. This density is then used in importance sampling for model estimation, model selection and model combination. The procedure is fully automatic which avoids difficult and time consuming tuning of MCMC strategies. The AdMitIS methodology is illustrated with an empirical application to S&P index log-returns. Several non-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns.

Keywords: GARCH; marginal likelihood; Bayesian model averaging; adaptive mixture of Student-t distributions; importance sampling (search for similar items in EconPapers)
JEL-codes: C11 C15 C22 (search for similar items in EconPapers)
Date: 2010-04-27
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