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Efficient Bayesian estimation and combination of GARCH-type models

David Ardia & Lennart Hoogerheide

Abstract 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
   
Citation Ardia, D., & Hoogerheide, L. (2010). Efficient Bayesian estimation and combination of GARCH-type models. In Rethinking Risk Measurement and Reporting (Vol. II, pp. 1-19). London: Klaus Bocker.
   
Type Book chapter (English)
Year 2010
Book title Rethinking Risk Measurement and Reporting
Publisher Klaus Bocker (London)
Volume II
Pages 1-19
Series title Risk Books
URL http://riskbooks.com/rethinking-risk-measurement-and-repo...