Efficient Bayesian Estimation and Combination of GARCH-Type Models
Rethinking Risk Measurement and Reporting: Examples and Applications from Finance, Vol. II, Chapter 1, Klaus Böcker, eds., RiskBooks, London, 2010
22 Pages Posted: 26 Jan 2010 Last revised: 15 Nov 2017
Date Written: January 25, 2010
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
JEL Classification: C11, C15, C22, C51
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