  A Bayesian estimation of a GARCH model is performed for US Dollar/Japanese
Yen exchange rate by the Metropolis-Hastings algorithm with a proposal density
given by the adaptive construction scheme. In the adaptive construction scheme
the proposal density is assumed to take a form of a multivariate Student's
t-distribution and its parameters are evaluated by using the sampled data and
updated adaptively during Markov Chain Monte Carlo simulations. We find that
the autocorrelation times between the data sampled by the adaptive construction
scheme are considerably reduced. We conclude that the adaptive construction
scheme works efficiently for the Bayesian inference of the GARCH model.
