On the Agricultural Commodities Supply Chain Resilience to Disruption: Insights from Financial Analysis

Abstract

The study proffers the need to strengthen supply chain resilience toward the minimization of disruptions. We investigate the financial performance of agricultural commodities' supply chain amid external uncertainty shocks in both time and frequency dimensions. The wavelet techniques are employed for the analysis. Specifically, both the Windowed Scalogram Difference (WSD) and Wavelet Multiple techniques are utilized. First, the study finds from the WSD that disruption which we proxy with global economic policy uncertainty (GEPU) hurts all the agricultural commodities in the medium and long terms. For instance, we find high comovements between agricultural raw material (ARM) and GEPU. This is similar to the comovements between rice and GEPU. Additionally, high potential comovements between 2005 and 2016 in the medium and long term for fertilizer, maize, and possibly wheat with the GEPU are found. Generally, there seem to be fewer comovements between soybeans and the GEPU which is indicative of a resistance to shocks. Second, we find from the Wavelet Multiple approaches that most of the agricultural commodities are highly integrated, except rice. Findings from the study imply minimization of diversification, hedge, or safe haven benefits with regard to market conditions among the commodities. The findings have important inferences for policymakers, supply chain actors of agricultural commodities, and investors to extensively examine the heterogeneous and adaptive dynamics of the commodity markets. We advocate that supply chains of agricultural commodities must be borne with dynamic capacities to manage vulnerabilities in an uncertain environment, necessitating resilience capabilities to thrive in the long run. Also, investors should hedge against the portfolio risk present in these agricultural commodities. This study is the first to provide insights into the supply chain of agricultural commodities from a financial perspective through time and/or frequency analysis.