Causes and Prospects of SCFI Fluctuations During the COVID Period

Y. Ha, S. Woo
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Abstract

Purpose – It is very important to forecast and analyze the shipping market in business operation and national policy establishment. However, it has become very difficult to forecast the shipping market cycle after COVID based on the balance of supply and demand as before. It is necessary to analyze this in a new way. Design/Methodology/Approach – In this study, we try to examine the relationship with freight using a new variable in a situation where traditional analysis is impossible. The basic analysis method of this study is panel regression analysis. In the basic formula, the COVID dummy variable was added to the independent variables, and the main variables and interaction terms were created to analyze the effect of economic indicators on shipping costs in the COVID situation. Findings – As a result of analyzing the impact of economic indicators on shipping costs after COVID, it was found that PPI and the Demand Forecasting Index had a negative effect on shipping costs. This is contrary to previous studies which found that shipping costs increase in proportion to economic growth or trade volume. However, economic instability increases the inflation index and the value of goods, and when inflation reaches its peak, the value of goods falls. Research Implications – This study is meaningful in that it analyzes the impact of the current situation on shipping costs, which may be difficult to analyze, and suggests the possibility of predictive research. If we apply this analysis and variables to predictive research, we will be able to derive meaningful results.
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新冠疫情期间SCFI波动的原因与展望
目的:对航运市场进行预测分析,对企业经营和国家政策制定具有重要意义。但是,很难像以前一样,以供需平衡为基础预测新冠疫情后的航运市场周期。有必要从新的角度来分析这个问题。设计/方法/方法-在本研究中,我们试图在传统分析不可能的情况下使用一个新变量来检查与运费的关系。本研究的基本分析方法是面板回归分析。在基本公式中,在自变量中加入COVID虚拟变量,创建主变量和交互项,分析COVID情况下经济指标对运输成本的影响。▽结果=分析了新冠疫情后经济指标对运费的影响,结果发现,PPI和需求预测指数对运费的影响为负。这与之前的研究相反,研究发现航运成本与经济增长或贸易量成正比。然而,经济不稳定会增加通货膨胀指数和商品价值,当通货膨胀达到顶峰时,商品价值就会下降。研究意义:本研究的意义在于分析了目前的情况对运输成本的影响,这可能是难以分析的,并提出了预测研究的可能性。如果我们将这种分析和变量应用于预测研究,我们将能够得出有意义的结果。
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