An Adaptive Markov Chain Monte Carlo Method for GARCH Model
Computational Finance
2009-07-14 v1 Statistical Mechanics
Statistical Finance
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.
Keywords
Cite
@article{arxiv.0901.0992,
title = {An Adaptive Markov Chain Monte Carlo Method for GARCH Model},
author = {Tetsuya Takaishi},
journal= {arXiv preprint arXiv:0901.0992},
year = {2009}
}
Comments
11 pages, 6 figures