An Adaptive Markov Chain Monte Carlo Method for GARCH Model

Tetsuya Takaishi

Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2

Research Article

An Adaptive Markov Chain Monte Carlo Method for GARCH Model

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@INPROCEEDINGS{10.1007/978-3-642-02469-6_22,
    author={Tetsuya Takaishi},
    title={An Adaptive Markov Chain Monte Carlo Method for GARCH Model},
    proceedings={Complex Sciences. First International Conference, Complex 2009, Shanghai, China, February 23-25, 2009, Revised Papers, Part 2},
    proceedings_a={COMPLEX PART 2},
    year={2012},
    month={5},
    keywords={Markov Chain Monte Carlo Bayesian inference GARCH model Metropolis-Hastings algorithm},
    doi={10.1007/978-3-642-02469-6_22}
}

Tetsuya Takaishi
Year: 2012
An Adaptive Markov Chain Monte Carlo Method for GARCH Model
COMPLEX PART 2
Springer
DOI: 10.1007/978-3-642-02469-6_22

Tetsuya Takaishi¹^,*

1: Hiroshima University of Economics

*Contact email: takaishi@hiroshima-u.ac.jp

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

Published: 2012-05-11

: http://dx.doi.org/10.1007/978-3-642-02469-6_22