A Markov-Chain Sampling Algorithm for GARCH Models

This paper describes a GAUSS program of a Markov-chain sampling algorithm for GARCH models proposed by Nakatsuma (1998). This algorithm allows us to generate Monte Carlo samples of parameters in a GARCH model from their joint posterior distribution. The samples obtained by this algorithm are used for Bayesian analysis of the GARCH model. As numerical examples, GARCH models of simulated data and of weekly foreign exchange rate series are estimated and analyzed.

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Year of publication:	2007
Authors:	Nakatsuma, Teruo
Published in:	Studies in Nonlinear Dynamics & Econometrics. - Berkeley Electronic Press. - Vol. 3.2007, 2, p. 107-117
Publisher:	Berkeley Electronic Press
Subject:	Bayesian inference \| GARCH \| Markov-chain Monte Carlo \| Metropolis-Hastings algorithm

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Extent:	application/pdf
Type of publication:	Article
Notes:	oai:bepress:
Source:	RePEc - Research Papers in Economics

Persistent link: https://ebvufind01.dmz1.zbw.eu/10004966247