交通运输系统工程与信息

交通运输系统工程与信息 ›› 2016, Vol. 16 ›› Issue (3): 28-34.

• 综合交通运输体系论坛 • 上一篇    下一篇

基于MCMC 算法AR-GARCH 模型中国出口 集装箱运价指数波动性研究

王思远1a，余思勤*1a，潘静静1b,2   

  1. 1. 上海海事大学a. 经济管理学院；b. 信息工程学院，上海201306； 2．福建农林大学交通与土木工程学院，福州350002
  • 收稿日期:2015-12-15 修回日期:2016-03-24 出版日期:2016-06-25 发布日期:2016-06-27
  • 作者简介:王思远（1989-），女，上海人，博士生.
  • 基金资助:

    国家自然科学基金青年项目/National Natural Science Foundation of China（71101088）；福建省社科规划项目/ Fujian Social Science Planning Project（FJ2015C107）.

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

WANG Si-yuan1a, YU Si-qin1a, PAN Jing-jing1b, 2   

  1. 1a. Economic and Management College; 1b. College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China；2. College of Transportation and Civil Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
  • Received:2015-12-15 Revised:2016-03-24 Online:2016-06-25 Published:2016-06-27

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被引次数

2

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WinBugs 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.

关键词: 水路运输, 波动持续性, AR-GARCH模型, CCFI, MCMC算法

Abstract:

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 by Bayesian beyond the sample is more predictable.

Key words: waterway transportation, volatility persistence, AR-GARCH model, CCFI, MCMC algorithm

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