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

See all articles by David Ardia

David Ardia

HEC Montreal - Department of Decision Sciences

Lennart F. Hoogerheide

VU University Amsterdam

Date Written: January 25, 2010

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

JEL Classification: C11, C15, C22, C51

Suggested Citation

Ardia, David and Hoogerheide, Lennart F., Efficient Bayesian Estimation and Combination of GARCH-Type Models (January 25, 2010). Rethinking Risk Measurement and Reporting: Examples and Applications from Finance, Vol. II, Chapter 1, Klaus Böcker, eds., RiskBooks, London, 2010, Available at SSRN: https://ssrn.com/abstract=1542253

David Ardia (Contact Author)

HEC Montreal - Department of Decision Sciences ( email )

3000 Côte-Sainte-Catherine Road
Montreal, QC H2S1L4
Canada

Lennart F. Hoogerheide

VU University Amsterdam ( email )

De Boelelaan 1105
Amsterdam, ND North Holland 1081 HV
Netherlands

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