Assessing the Effect of the Magnitude of Spillovers on Global Supply Chains Using Quantile Vector Autoregressive and Wavelet Approaches

Abstract

Overwhelmed by the negative impacts of the COVID-19 pandemic, global supply chains are being restructured and improved worldwide. It then becomes essential to accurately assess their vulnerabilities to external shocks and understand the relationships between key influential factors to obtain the desired results. This study provides a new conceptual econometric framework to examine the relationships between the purchasing managers’ index, service purchasing managers’ index, world equity index, unemployment rate, food and beverage historical prices, Baltic Dry Index, West Texas Intermediate Index, and carbon emissions. A quantile vector autoregressive (QVAR) model is used to assess the dynamic connectedness among Brazil, Russia, India, China, South Africa, and the United States based on such factors. A wavelet method is also utilized to assess the coherence between the time series. The results of the correlation and dynamic connectedness analyses for these countries reveal that the service purchasing managers’ index offers the highest spillover value toward the other factors.