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. © 2009 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
CITATION STYLE
Takaishi, T. (2009). An adaptive Markov chain Monte Carlo method for GARCH model. In Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering (Vol. 5 LNICST, pp. 1424–1434). https://doi.org/10.1007/978-3-642-02469-6_22
Mendeley helps you to discover research relevant for your work.