Soybeans are a strategic commodity in global agricultural trade, and disruptions to their pricing system have direct implications for food security and trade patterns. This paper examines how major external shocks, particularly the Sino–U.S. trade wars, reshaped the dynamic connectedness and risk transmission structure of the global soybean price system. Using daily data from 2015–2025 for five key benchmarks, Chicago Board of Trade (CBOT) soybean futures, Dalian Commodity Exchange (DCE) No. 1 soybean futures, and cost-and-freight (CNF) prices for U.S. Gulf, Brazil, and Argentina shipments to China, we apply the time-varying parameter vector autoregression Diebold–Yilmaz connectedness model (TVP-VAR-DY) and the time-varying parameter vector autoregression Baruník–Křehlík frequency connectedness model (TVP-VAR-BK) models to quantify time-varying spillovers across short-, medium-, and long-run horizons. The results indicate that the global soybean market is highly integrated, while systemic risk transmission is predominantly short-run and declines sharply at longer horizons. CBOT futures remain the principal source of spillovers, especially in the short term, yet their net influence weakens noticeably after the 2018 trade-friction episode and declines further following the 2025 episode, particularly with respect to South American CNF benchmarks. Frequency-specific evidence suggests that trade-policy escalations are increasingly priced as structural shocks, strengthening medium- and long-horizon connectedness, while DCE’s outward spillovers rise markedly around 2025, consistent with the emergence of a more regionalized pricing architecture centered on Chinese demand. Within South America, Brazil leads short-run price formation, whereas longer-horizon dynamics are more exposed to Argentine policy risk spillovers. These findings provide new evidence on supply-chain reconfiguration and benchmark rebalancing in global soybean pricing and offer policy implications for strengthening China’s pricing capacity and enhancing multi-horizon supply-chain risk management.
Zhang et al. (Mon,) studied this question.