Random Matrix Theory Analysis of Global Agricultural Futures Before, During, and After the COVID-19 Pandemic

We use Random Matrix Theory to analyze the impact of COVID-19 on the correlation structure of the agricultural futures market, using daily returns for 56 futures from various countries around the world before, during, and after COVID. For each of the three time periods, we build correlation matrices of the daily returns, and we examine the matrices’ entries, eigenvalues, and eigenvectors. We find that agricultural futures exhibited stronger collective behavior during COVID than before or after it. We also find clustering based on geographic location, with there being one cluster in the West (North America, South America, Europe) and one in the East (Asia, Oceania), which show predominantly opposite-signed loadings relative to the dominant market mode; since pre-pandemic times, the East has gained weight in the market while the West has lost it. On top of regional clustering, we also find independent exchange-level clustering, where futures tend to be more correlated with futures in its same exchange than any other. Our results demonstrate how Random Matrix Theory can reveal both transient changes in collective behavior and longer-term structural patterns in an interconnected global market.