Forecasting ethanol price volatility under structural breaks

Abstract

The use of ethanol as a vehicle fuel has reduced greenhouse gas emissions significantly. The introduction of ethanol has also led to a decrease in crude oil prices. Considering the economic and environmental significance of the biofuel markets, a strand of literature investigates the price and volatility dynamics of US ethanol prices. In this paper, in contrast to previous studies, we investigate whether information on structural breaks plays an important role in predicting US ethanol market volatility. Our findings reveal that generalized autoregressive conditional heteroskedasticity (GARCH) models incorporating these breaks improve the prediction of US ethanol market volatility. Furthermore, the persistence of volatility tends to decline when structural breaks are included in the GARCH models. We further note that the influence of good and bad news is properly assessed under such breaks. Our results suggest that ignoring such breaks could mislead the risk assessment procedure for the US biofuel industry.