Efficient Bayesian Estimation and Combination of GARCH-Type Models

Arbeitspapier

This paper 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. Several non-nested GARCH-type models are estimated and combined to predict the distribution of next-day ahead log-returns.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: Tinbergen Institute Discussion Paper ; No. 10-046/4

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Bayesian Analysis: General ; Statistical Simulation Methods: General ; Single Equation Models; Single Variables: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
• Thema: GARCH ; marginal likelihood ; Bayesian model averaging ; adaptive mixture of Student-t distributions ; importance sampling ; ARCH-Modell ; Bayes-Statistik ; Statistische Verteilung ; Statistisches Auswahlverfahren ; Theorie

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Ardia, David ; Hoogerheide, Lennart F.
• Ereignis: Veröffentlichung
• (wer): Tinbergen Institute
• (wo): Amsterdam and Rotterdam
• (wann): 2010

Weitere Informationen

• Handle: http://hdl.handle.net/10419/86670
• Letzte Aktualisierung: 10.03.2025, 11:41 MEZ