论文标题
贝叶斯非参数小组马尔可夫开关GARCH模型
Bayesian nonparametric panel Markov-switching GARCH models
论文作者
论文摘要
本文介绍了一种新的模型，用于带有马尔可夫开关GARCH效果的面板数据。该模型结合了一个串联的隐藏马尔可夫链过程，该过程驱动GARCH参数。为了应对参数空间的高维度，该论文通过首先假设通过层次的先验分布进行两个步骤过程来利用系列的横截面聚类，然后通过非参数先验分布在参数空间中引入聚类效应。通过模拟实验评估了模型和所提出的推理。结果表明，推理能够恢复参数的真实值和每个制度中的组数。 SP \＆100索引的78个资产的经验应用程序也从2000年1月到$ 3^{rd} $ 2020 $ 3^{rd} $也是通过使用Twien-Regime Markov Switching Garch型号进行的。这些发现分别表明了在第一和第二条制度中分别存在2和3个簇。
This paper introduces a new model for panel data with Markov-switching GARCH effects. The model incorporates a series-specific hidden Markov chain process that drives the GARCH parameters. To cope with the high-dimensionality of the parameter space, the paper exploits the cross-sectional clustering of the series by first assuming a soft parameter pooling through a hierarchical prior distribution with two-step procedure, and then introducing clustering effects in the parameter space through a nonparametric prior distribution. The model and the proposed inference are evaluated through a simulation experiment. The results suggest that the inference is able to recover the true value of the parameters and the number of groups in each regime. An empirical application to 78 assets of the SP\&100 index from $6^{th}$ January 2000 to $3^{rd}$ October 2020 is also carried out by using a two-regime Markov switching GARCH model. The findings shows the presence of 2 and 3 clusters among the constituents in the first and second regime, respectively.
