A Markov-Chain Sampling Algorithm for GARCH Models

Teruo Nakatsuma

Studies in Nonlinear Dynamics & Econometrics, 1998, vol. 3, issue 2, 13

Abstract: 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.

Keywords: Bayesian inference; GARCH; Markov-chain Monte Carlo; Metropolis-Hastings algorithm (search for similar items in EconPapers)
Date: 1998
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Citations: View citations in EconPapers (13)

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DOI: 10.2202/1558-3708.1043

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