In this paper, we use a logit model to predict the probability of default for Korean shipping companies. We explore numerous financial ratios to find predictors of a shipping firm’s failure and construct four default prediction models. The results suggest that a model with industry specific indicators outperforms other models in predictive ability. This finding indicates that utilizing information about unique financial characteristics of the shipping industry may enhance the performance of default prediction models. Given the importance of the shipping industry in the Korean economy, this study can benefit both policymakers and market participants.

Using time-series data from January 2006 to February 2021, this study analyzed the effect of macroeconomic shocks on the shipping and shipbuilding industries. The Granger causality test, recursive structural vector autoregressive models, impulse response analysis, historical decomposition, and local projections model were used to identify the dynamic relationships between the variables and their dynamic effects, based on the results of the theoretical model and previous research. First, the Granger causality test demonstrated that the macroeconomic variables have causal relations with the shipping and shipbuilding industries. Second, the recursive structural vector autoregressive estimation demonstrated that the direction of the shocks from macroeconomic variables is statistically significantly, consistent with the theoretical model. The same results were found in the recursive structural vector autoregressive model and local projection impulse response analysis. Finally, the historical decomposition identified the main causal variables affecting the shipping and shipbuilding industries by period. These findings can help policymakers, operators of shipping and shipbuilding companies, and investors evaluate and make policy-supporting decisions on industry conditions.

This study examined the intricate effects of the COVID-19 pandemic on international trade complexity. Focusing on major international trading entities such as Hong Kong, Korea, China, Japan, and the U.S., we evaluated how confirmed COVID-19 cases and government responses influenced trade patterns, particularly in exports and imports. We employed a seemingly unrelated regression (SUR) analysis with cyclic regression coefficients to scrutinize changes in trade relationships between 2020 and 2021. Korea serves as the central country for this analysis, and the findings extend to other nations. The results revealed the varied impact of COVID-19 on trade across countries. Exports from Korea to China were correlated with COVID-19 case numbers and government actions in both countries. Additionally, imports from China, Japan, the U.S. and Vietnam—excluding Hong Kong—were significantly influenced by confirmed COVID-19 cases in Korea, reflecting the demand dynamics. Government interventions also played a substantial role in shaping trade patterns. Previous studies have primarily focused on financial markets and supply chains, whereas our study analyzed the changes in trade with Korea’s five largest trading partners: China, the U.S., Japan, Vietnam, and Hong Kong. Notably, we utilized long-term data and changes in major trading partners in Asia over time.