GARCH模型的自适应马尔可夫链蒙特卡罗方法
An Adaptive Markov Chain Monte Carlo Method for GARCH Model
摘要：
我们提出了一种方法来构建一个建议密度的大都市-黑斯廷斯算法在马尔可夫链蒙特卡罗（MCMC）模拟的GARCH模型。建议密度是利用MCMC方法本身采样的数据自适应地构造的。事实证明，使用我们的自适应建议密度生成的数据之间的自相关性大大降低。因此，自适应构造方法是非常有效的，并适用于GARCH模型的MCMC模拟。
英文摘要：
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.
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