标题： 基于自适应构造方案的非对称GARCH模型的马尔可夫链蒙特卡罗方法 显示英文标题

标题： Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive Construction Scheme

摘要： 我们对GJR-GARCH模型进行马尔可夫链蒙特卡洛模拟，以进行贝叶斯推断，GJR-GARCH模型是不对称GARCH模型之一。 在Metropolis-Hastings算法中使用自适应构造方案来构建提议密度，并通过使用马尔可夫链蒙特卡洛模拟采样的数据自适应地确定提议密度的参数。 我们研究了该方案在人工GJR-GARCH数据上的性能。 我们发现自适应构造方案能够有效地采样GJR-GARCH参数，并得出结论，即带有自适应构造方案的Metropolis-Hastings算法是GJR-GARCH模型贝叶斯推断的一种有效方法。 显示英文摘要

摘要： We perform Markov chain Monte Carlo simulations for a Bayesian inference of the GJR-GARCH model which is one of asymmetric GARCH models. The adaptive construction scheme is used for the construction of the proposal density in the Metropolis-Hastings algorithm and the parameters of the proposal density are determined adaptively by using the data sampled by the Markov chain Monte Carlo simulation. We study the performance of the scheme with the artificial GJR-GARCH data. We find that the adaptive construction scheme samples GJR-GARCH parameters effectively and conclude that the Metropolis-Hastings algorithm with the adaptive construction scheme is an efficient method to the Bayesian inference of the GJR-GARCH model.