Xu Lang, Zou Zeyuan, Zhou Shaorui
College of Transport and Communications, Shanghai Maritime University, Shanghai, PR China.
College of Management, Shenzhen University, Shenzhen, Guangdong, PR China.
Ocean Coast Manag. 2022 Oct 1;229:106330. doi: 10.1016/j.ocecoaman.2022.106330. Epub 2022 Aug 23.
In this study, we use the sample data from Jan 22, 2020 to Jan 21, 2022 to investigate the impacts of added infection number on the volatility of BDI. Under this structure, the control variables (freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls) are added to test whether the information contained in the added infection number is covered. In the GARCH-MIDAS model, we divide the volatility of BDI into the long-term and short-term components, then employ in the least squares regression to empirically test the influences of added infection number on the volatility. From the analysis, we find the added infection numbers effectively impact the BDI volatility. In addition, whether the freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls and other variables are considered alone or at the same time, further the added infection number still significantly influences the volatility of BDI. By studying the ability of the confirmed number to explain the volatility of BDI, a new insight is provided for the trend prediction of BDI that the shipping industry can take the epidemic development of various countries as a reference to achieve the purpose of cost or risk control.
在本研究中，我们使用2020年1月22日至2022年1月21日的样本数据，来研究新增感染数对波罗的海干散货运价指数（BDI）波动性的影响。在此结构下，加入控制变量（运费、布伦特原油价格、集装箱闲置率、港口拥堵水平、全球港口停靠次数），以检验新增感染数中所包含的信息是否已涵盖在内。在广义自回归条件异方差混合数据抽样（GARCH-MIDAS）模型中，我们将BDI的波动性分为长期和短期成分，然后采用最小二乘法回归，实证检验新增感染数对波动性的影响。通过分析，我们发现新增感染数对BDI波动性有显著影响。此外，无论单独考虑还是同时考虑运费、布伦特原油价格、集装箱闲置率、港口拥堵水平、全球港口停靠次数等变量，新增感染数仍对BDI波动性有显著影响。通过研究确诊数对BDI波动性的解释能力，为BDI的趋势预测提供了新的视角，即航运业可以参考各国疫情发展情况，以达到成本或风险控制的目的。
