Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/86670
Authors: 
Ardia, David
Hoogerheide, Lennart F.
Year of Publication: 
2010
Series/Report no.: 
Tinbergen Institute Discussion Paper 10-046/4
Abstract: 
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.
Subjects: 
GARCH
marginal likelihood
Bayesian model averaging
adaptive mixture of Student-t distributions
importance sampling
JEL: 
C11
C15
C22
Document Type: 
Working Paper

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