Fat tails, serial dependence, and interlinkages of the geopolitical risk and food market during the COVID-19 pandemic and war crisis: an application of Bayesian vector heterogeneous autoregressions.

Abstract

We investigate fat tails and network interconnections of geopolitical risk index and food prices, including the price of corn, rice, and wheat, using seven Bayesian vector heterogeneous autoregression fashions. This paper differentiates dynamically between network interlinkages between these variables during the short, medium, and long runs. We found some noteworthy results in our study. In the first place, network interlinkages exhibit remarkable differences over time. Interlinkages between variables in our designed networks are increased in the short, medium, and long term due to transient events occurring in markets during the studied period. During the Russia-Ukraine conflict, the long-term ties within the system are more significantly impacted. Additionally, based on net-directional linkages, each market's role shifts (from sending to receiving shock and vice versa) during the pre- and post-Ukraine-Russia conflict, whereas these roles persist during the COVID-19 pandemic. Observations of short- and medium-term trends reveal that the geopolitical risk index is shock receivers transmitted to these markets by the rice and corn markets. The results indicate that the geopolitical risk index persists as shock receivers in terms of long-horizon measures.