An Adaptive Markov Chain Monte Carlo Method for GARCH Model

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 metho d 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.

11 pages, 6 figures

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Cited by in corpus (7)

• Bayesian inference with an adaptive proposal density for GARCH models
• Bayesian estimation of GARCH model with an adaptive proposal density
• Analysis of Spin Financial Market by GARCH Model
• Bayesian Inference on QGARCH Model Using the Adaptive Construction Scheme
• Empirical Study of the GARCH model with Rational Errors
• Empirical Analysis of Stochastic Volatility Model by Hybrid Monte Carlo Algorithm
• The relationship between trading volumes, number of transactions, and stock volatility in GARCH models