EconPapers    
Economics at your fingertips  
 

Bayesian inference on GARCH models using the Gibbs sampler

Luc Bauwens () and Michel Lubrano ()

Econometrics Journal, 1998, vol. 1, issue ConferenceIssue, C23-C46

Abstract: This paper explains how the Gibbs sampler can be used to perform Bayesian inference on GARCH models. Although the Gibbs sampler is usually based on the analyti-cal knowledge of the full conditional posterior densities, such knowledge is not available in regression models with GARCH errors. We show that the Gibbs sampler can be combined with a unidimensional deterministic integration rule applied to each coordinate of the poste-rior density. The full conditional densities are evaluated and inverted numerically to obtain random draws of the joint posterior. The method is shown to be feasible and competitive compared with importance sampling and the Metropolis-Hastings algorithm. It is applied to estimate an asymmetric Student-GARCH model for the return on a stock exchange index, and to compute predictive option prices on the index. We prove, moreover, that a flat prior on the degrees of freedom parameter leads to an improper posterior density.

Keywords: Bayesian inference; GARCH; Gibbs sampler; Monte Carlo; Option pricing. (search for similar items in EconPapers)
Date: 1998
References: Add references at CitEc
Citations: View citations in EconPapers (73) Track citations by RSS feed

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Working Paper: Bayesian inference on GARCH models using the Gibbs sampler (1998) Downloads
Working Paper: Bayesian Inference on GARCH Models using the Gibbs Sampler (1996) Downloads
Working Paper: Bayesian Inference on GARCH Models Using the Gibbs Sampler (1996)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c23-c46

Ordering information: This journal article can be ordered from
http://www.ectj.org

Access Statistics for this article

Econometrics Journal is currently edited by Richard J. Smith, Oliver Linton, Pierre Perron, Jaap Abbring and Marius Ooms

More articles in Econometrics Journal from Royal Economic Society Contact information at EDIRC.
Bibliographic data for series maintained by Wiley-Blackwell Digital Licensing ().

 
Page updated 2019-10-12
Handle: RePEc:ect:emjrnl:v:1:y:1998:i:conferenceissue:p:c23-c46