基于MCMC的分位回归AR-ARCH模型的贝叶斯分析

Bayesian analysis of the quantile AR-ARCH models based on MCMC algorithm

针对时间序列分布特征的高峰厚尾性,提出了一类分位回归ARCH模型.在贝叶斯理论框架下,通过选择适当的先验分布,并基于非对称Laplace分布构建模型的似然函数,实现了模型的贝叶斯推断.仿真试验和分析表明,该分位回归ARCH模型可全面刻画时间序列的非对称性和高峰厚尾性.

Since many time series with asymmetric and heavier tails,we adapt the quantile regression ideas to the ARCH models.In the framework of Bayesian theory,we employ the proper prior,the likelihood function based on the asymmetric Laplace distribution was employed irrespective of the original distribution of the data,and derive the posterior distribution of the model parameters.The simulation result shows that the quantile ARCH models are effective to capture the diversity of time series distribution.