Efficient Bayesian estimation and combination of GARCH-type models
David Ardia & Lennart Hoogerheide
| 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. |
| Mots-clés |
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 | Chapitre de livre (Anglais) |
| Année | 2010 |
| Titre du livre | Rethinking Risk Measurement and Reporting |
| Editeur commercial | Klaus Bocker (London) |
| Volume | II |
| Pages | 1-19 |
| Titre de la collection | Risk Books |
| URL | http://riskbooks.com/rethinking-risk-measurement-and-repo... |