Bayesian Nonparametric Panel Markov-Switching GARCH Models

This article proposes Bayesian nonparametric inference for panel Markov-switching GARCH models. The model incorporates series-specific hidden Markov chain processes that drive the GARCH parameters. To cope with the high-dimensionality of the parameter space, the article assumes soft parameter pooling through a hierarchical prior distribution and introduces cross sectional clustering through a Bayesian nonparametric prior distribution. An MCMC posterior approximation algorithm is developed and its efficiency is studied in simulations under alternative settings. An empirical application to financial returns data in the United States is offered with a portfolio performance exercise based on forecasts. A comparison shows that the Bayesian nonparametric panel Markov-switching GARCH model provides good forecasting performances and economic gains in optimal asset allocation.

本文针对面板马尔可夫转换广义自回归条件异方差（Generalized Autoregressive Conditional Heteroskedasticity，GARCH）模型提出贝叶斯非参数推断方法。该模型纳入了驱动GARCH参数的序列专属隐马尔可夫链过程。为应对参数空间的高维性难题，本文通过分层先验分布采用软参数池化设定，并借助贝叶斯非参数先验分布引入截面聚类机制。本文提出马尔可夫链蒙特卡洛（Markov Chain Monte Carlo，MCMC）后验近似算法，并在多种替代设定下通过仿真实验验证了该算法的效率。本文基于美国金融收益数据开展实证应用，并结合预测结果开展投资组合绩效分析。对比实验表明，贝叶斯非参数面板马尔可夫转换GARCH模型在最优资产配置任务中具备优异的预测性能与经济收益。