The pattern of international agricultural trade is undergoing profound changes. The influence of country risks on the international agricultural trade pattern is prominent. In this paper, we comprehensively analyze the international agricultural trade patterns and explore the influence of country risks on them. Specifically, we first construct an international agricultural trade network (IATN) based on complex network theory. Second, we analyze each country’s diversity of import sources and the position of countries in the IATN using the Herfindahl–Hirschman Index (HHI) and network indicators, such as in-degree, out-degree, weighted in-degree, weighted out-degree, and betweenness centrality. Third, this paper explores the influence of different types of country risks, including economic risk and political risk, on international agricultural trade patterns using the panel regression method. The results show that countries played different roles and occupied different positions in the international agricultural trade pattern; notably, the United States occupied a core position, while Japan and Mexico had insufficient diversity in import sources. Moreover, based on the panel regression method, we find that political risks have a positive impact on the agricultural trade pattern, while an unstable economic environment could inhibit the agricultural trade pattern in various countries. This study could provide references for countries to implement agricultural trade policies regarding country risks to ensure stable agricultural trade relations and national food security.

Cobalt is a key resource for the global energy transition, and the differences in the natural endowment of cobalt have led to frequent cobalt trade among countries. This study aims to reveal the dependence patterns of cobalt trade among countries and the impact of country risks (including political and economic risks) on the patterns. First, a cobalt import dependence network (CIDN) and a cobalt export dependence network (CEDN) are established using the network analysis method. Furthermore, this study uses network indicators to reveal the dependence patterns of cobalt trade among countries, and construct diversification indices of trade relations to further analyze the import source risk and the market concentration of cobalt trade. The results indicate that most cobalt importers have a high import source risk, and most cobalt exporters have a high market concentration. Finally, based on the panel regression methods, we reveal an interesting result showing that the dependence patterns of cobalt trade are significantly influenced by country risks. Specifically, on the one hand, for importers, an increase in political risk or economic risk has a negative impact on their dependence patterns of cobalt trade. On the other hand, for exporters, an increase in political risk or economic risk has different effects on their dependence patterns of cobalt trade. This study suggests that countries should pay more attention to the role of country risks in driving the dependence patterns when making cobalt trade policies.

Financial stocks in the industry chain interact notably because of close economic and technical relationships. Some participants pay particular attention to one industry chain and are concerned with different investment horizons. The motivation for this study is to offer more targeted information to various market participants who focus on different time scales in one industry chain from a systematic perspective by combining the GARCH-BEKK, heterogeneous network, and wavelet analysis methods. The findings are as follows: (1) For parties who prefer to take more risks to gain higher returns, scale 2 (4–8 days) is a good option, while long-term investment (32–128 days) is suitable for conservative investors. (2) In most cases, some links in the industry chain are particularly sensitive to changes in stocks in other links. (3) The influence, sensitivity, and intermediary of stocks in the industry chain on different time scales were explored, and participants could use the resulting information to monitor the market or select stocks. (4) The structures, key players, and industry chain attributes of the main transmission paths differ on multi-time scales. Risk transmission can be controlled by intercepting important spillover relations within the paths.