非对称GARCH模型自适应构造方案的Markov链蒙特卡罗
Markov Chain Monte Carlo on Asymmetric GARCH Model Using the Adaptive Construction Scheme
摘要：
我们执行马尔可夫链蒙特卡罗模拟的贝叶斯推断的GJR- GARCH模型，这是一个不对称的GARCH模型。在Metropolis-Hastings算法中采用自适应构造方案构造建议密度，并利用马尔可夫链蒙特卡罗模拟采样数据自适应地确定建议密度的参数。我们研究了该方案的性能与人工GJR-Gesterfield数据。结果表明，自适应构造方案能有效地对GJR-Gestern模型参数进行采样，并证明了采用自适应构造方案的Metropolis-Hastings算法是一种有效的GJR- GARCH模型贝叶斯推断方法。
英文摘要：
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.
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