Bayesian analysis of the quantile AR-ARCH models based on MCMC algorithm
Huifan Zeng · Journal of Yanbian University · 2014
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.