Impact of local port disruption on global container trade: An example of stressing testing Chinese ports using a D-vine copula-based quantile regression

Abstract

In the shipping market, ports are increasingly interconnected in the global container network. A port disruption in one place can lead to a chain effect on subsequent ports. Nowadays, China's container port throughput accounts for around 30% of the global port throughput, manifesting its essential role in the global container trade system. Therefore, any disruption in Chinese ports could have a cascading effect on the world container trade. This study explores the potential impact on the container main trading lanes given a stress scenario for a number of Chinese ports. We are particularly interested in the degree of risk spillover to the different trading lanes. The stress scenario used in this study is a severe drop in the container throughput in major Chinese ports. This drop will be reflected in quantiles of the respective throughput distributions that are close to zero. D-vine copula-based quantile regression is used to measure the throughput impact of stressed Chinese ports as covariates on other response container trading routes. Copula is a powerful tool to measure the tail dependence structure between throughputs of major Chinese ports and major container trading routes. Results show that port disruptions in China severely impact the major container trading routes. The Asia to Sub Saharan Africa route is extremely vulnerable to downside risk given any port disruption event in China. We have also compared the results with the historical trade and port call data. The results provide significant implications for container shipping risk management.