Efficient Bayesian estimation and combination of GARCH-type models
David Ardia and
Lennart F. Hoogerheide
MPRA Paper from University Library of Munich, Germany
This chapter 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 where non-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns.
Keywords: GARCH; Bayesian inference; MCMC; marginal likelihood; Bayesian model averaging; adaptive mixture of Student-t distributions; importance sampling. (search for similar items in EconPapers)
JEL-codes: C51 C22 C15 C11 (search for similar items in EconPapers)
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https://mpra.ub.uni-muenchen.de/22919/1/MPRA_paper_22919.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/27854/2/MPRA_paper_27854.pdf revised version (application/pdf)
Working Paper: Efficient Bayesian Estimation and Combination of GARCH-Type Models (2010)
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:22919
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