Bidirectional Extreme Risk Spillover Effect Between Ethanol and Agricultural Commodities – Robust Linear Quantile Regression Approach

Abstract The demand for green energy has become a critical priority in today’s world, where ethanol plays an important role. However, global factors contribute to significant price fluctuations in both ethanol and agricultural markets, leading to extreme risks. This study examines the bidirectional extreme risk spillover effect between ethanol and four key agricultural markets: corn, wheat, soybeans, and sugar. Extreme risk is quantified using CVaR, with dynamic CVaR time series generated through the long-memory FIAPARCH model. The spillover effect is assessed using an innovative robust linear quantile regression method. Our findings show that corn and soybeans exert the strongest extreme risk impact on ethanol, particularly during periods of high volatility. In contrast, the impact from the wheat market is considerably weaker, and no spillover effect is observed from the sugar market. When looking at the reverse relationship, agricultural markets are only minimally affected by extreme risk from the ethanol market, with no spillover detected in the wheat and sugar markets. A complementary portfolio analysis highlights that soybeans provide the best risk reduction for ethanol, as they are the least volatile agricultural commodity.