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Efficient Bayesian estimation and combination of GARCH-type models
Auteur(s)
Hoogerheide, Lennart
Maison d'édition
London: Klaus Bocker
Date de parution
2010
In
Rethinking Risk Measurement and Reporting
No
II
De la page
1
A la page
19
Collection
Risk Books
Résumé
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.
Identifiants
Type de publication
book part
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