Systemic Risk in Supply Chains: A Vector Autoregressive Measurement Approach Based on the Example of Automotive and Semiconductor Supply Chains

Supply chain failures and supply shortages have always been a matter of high risk. Especially when considering the scope and velocity of modern supply chains, small disturbances can cause immense damage. However, a framework for quantifying supply chain systemic risk is still missing.

To address this, we use the principles of the Diebold and Yilmaz connectedness approach, which is based on assessing the decomposition of the forecast error variance of a vector autoregressive (VAR) model, and adjust it to supply chains. By doing so, we seek to establish a systemic risk measurement of individual supply chains on different aggregation levels. In detail, we examine the automotive and semiconductor supply chains. Looking at specific firms, we identify vulnerable nodes and hubs of these supply chains and, thus, can measure the risk exposure originated by a certain region or supply chain level.

Our results show that for both supply chains, risk spillovers were at their highest levels during the COVID-19 pandemic, and firms facing U.S. trade restrictions experienced particularly strong effects during our sample period. In general, our approach provides convincing results, since companies identified as particularly risky are in line with specific company news that indicate risky spillovers during the study period.