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An Adaptive Markov Chain Monte Carlo Method for GARCH Model

  • Tetsuya Takaishi
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 5)

Abstract

We propose a method to construct a proposal density for the Metropolis-Hastings algorithm in Markov Chain Monte Carlo (MCMC) simulations of the GARCH model. The proposal density is constructed adaptively by using the data sampled by the MCMC method itself. It turns out that autocorrelations between the data generated with our adaptive proposal density are greatly reduced. Thus it is concluded that the adaptive construction method is very efficient and works well for the MCMC simulations of the GARCH model.

Keywords

Markov Chain Monte Carlo Bayesian inference GARCH model Metropolis-Hastings algorithm 

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Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Tetsuya Takaishi
    • 1
  1. 1.Hiroshima University of EconomicsHiroshimaJapan

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