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<p>Global supply chains increasingly depend on financial mechanisms that support liquidity and trade credit across borders; yet, macro-financial instability may disrupt these arrangements. This study examines how exchange rate volatility, cross-border credit availability, and global currency fragmentation are associated with trade credit-based supply chain financing among European and African firms, while also considering the moderating role of domestic credit to the private sector. Using a balanced panel of 40 firms over the period 2010–2024 (600 firm-year observations), the study adopts a multi-method empirical framework combining two-way fixed effects, dynamic system-GMM estimation, and panel quantile regression to capture static, dynamic, and heterogeneous relationships. In addition, a machine learning approach based on extreme gradient boosting (XGBoost) and SHAP analysis is employed to examine predictive patterns and nonlinearities. The results show that exchange rate volatility and global currency fragmentation are negatively associated with trade credit intensity, whereas cross-border credit availability is positively associated with firms’ reliance on supplier-based financing within global production networks. Domestic financial development attenuates the negative association between exchange rate volatility and trade credit financing, highlighting the role of financial intermediation in mitigating external shocks. The quantile regression results reveal that these relationships vary across firms with different levels of trade credit dependence, indicating the presence of distributional heterogeneity. The machine learning analysis identifies exchange rate volatility, cross-border credit availability, and leverage as key predictors of trade credit intensity, providing complementary insights into nonlinear relationships. The study contributes to the literature by integrating macro-financial conditions and firm-level characteristics within a unified empirical framework, while combining econometric and predictive approaches to examine financing behaviour in global supply chains. The findings offer policy-relevant insights on the importance of financial stability, domestic credit development, and international liquidity in supporting resilient supply chain financing structures.</p>

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Exchange rate volatility, global currency fragmentation, and supply chain financing: evidence from European and African firms

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月11日

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  • Akpobome Gregory Akpoyibo,
  • Smith Maxwell Ogbotor,
  • Abdulgaffar Muhammad,
  • Edirin Jeroh,
  • Michael Tonbraladoh Sinebe,
  • Emmanuela Nneka Clinton,
  • Maryam Isyaku,
  • Fatima Adam Labaran,
  • Bernard Osahon Odiase,
  • Emmanuel Osarowenyeke Ogbebor

摘要

Global supply chains increasingly depend on financial mechanisms that support liquidity and trade credit across borders; yet, macro-financial instability may disrupt these arrangements. This study examines how exchange rate volatility, cross-border credit availability, and global currency fragmentation are associated with trade credit-based supply chain financing among European and African firms, while also considering the moderating role of domestic credit to the private sector. Using a balanced panel of 40 firms over the period 2010–2024 (600 firm-year observations), the study adopts a multi-method empirical framework combining two-way fixed effects, dynamic system-GMM estimation, and panel quantile regression to capture static, dynamic, and heterogeneous relationships. In addition, a machine learning approach based on extreme gradient boosting (XGBoost) and SHAP analysis is employed to examine predictive patterns and nonlinearities. The results show that exchange rate volatility and global currency fragmentation are negatively associated with trade credit intensity, whereas cross-border credit availability is positively associated with firms’ reliance on supplier-based financing within global production networks. Domestic financial development attenuates the negative association between exchange rate volatility and trade credit financing, highlighting the role of financial intermediation in mitigating external shocks. The quantile regression results reveal that these relationships vary across firms with different levels of trade credit dependence, indicating the presence of distributional heterogeneity. The machine learning analysis identifies exchange rate volatility, cross-border credit availability, and leverage as key predictors of trade credit intensity, providing complementary insights into nonlinear relationships. The study contributes to the literature by integrating macro-financial conditions and firm-level characteristics within a unified empirical framework, while combining econometric and predictive approaches to examine financing behaviour in global supply chains. The findings offer policy-relevant insights on the importance of financial stability, domestic credit development, and international liquidity in supporting resilient supply chain financing structures.