Dynamic Volatility of China Containerized Freight Index Based on MCMC Algorithm of AR-GARCH Model

China has a large number of importing and exporting containerized cargo. Shippers and shipping companies face enormous risks from liner freight rates volatility. An AR-GARCH model is proposed to capture dynamic volatility of CCFI with Griddy-Gibbs sampling applied to simulate in WinBUGS. CCFI weekly is from April 1998 to December 2013. The empirical results of MCMC algorithm to a Bayesian inference show that the AR(3)-GARCH(1,1) model well fit the data. The strong persistence of volatility is reflected by the estimations, but no risk-premium or leverage effects. Results show that AR-GARCH-T model has better fitting effect. The AR-GARCH-T model estimated by ML within the sample is more fitting,