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中文摘要：
我国是集装箱货物进出口大国,集装箱班轮运价的剧烈波动使货主和班轮公司面临巨大的风险.为研究我国出口集装箱运价波动风险,对中国出口集装箱运价指数（China Containerized Freight Index,CCFI）建立基于Griddy-Gibbs抽样MCMC算法的贝叶斯AR-GARCH模型.针对1998年4月至2013年12月的CCFI总指数的去均值周收益率数据,建立残差基于正态分布和T分布的AR-GARCH模型,运用Win Bugs软件和MH算法进行贝叶斯参数估计,发现AR（3）-GARCH（1,1）模型拟合效果最好;参数估计结果表明,波动具有较强的持续性,不存在“风险溢价”和“杠杆效应”.经对比,发现AR-GARCH-T模型拟合效果更好;对比ML方法,发现MCMC算法估计结果的样本内拟合优度较差,而样本外预测能力较强.
英文摘要：
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, while the counter party inferred bv Bayesian beyond the sample is more predictable.
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