Food versus fuel: Causality and predictability in distribution - Andrea Bastianin; Marzio Galeotti; Matteo Manera (Fondazione Eni Enrico Mattei (FEEM), 2013)
This paper examines the relationship between biofuels and commodity food prices in the U.S. from a new perspective. While a large body of literature has tried to explain the linkages between sample means and volatilities associated with ethanol and agricultural price returns, little is known about their whole distributions. We focus on predictability in distribution by asking whether ethanol returns can be used to forecast different parts of field crops returns distribution, or vice versa. Density forecasts are constructed using Conditional Autoregressive Expectile models estimated with Asymmetric Least Squares. Forecast evaluation relies on quantile-weighed scoring rules, which identify regions of the distribution of interest to the analyst. Results show that both the centre and the left tail of the ethanol returns distribution can be predicted by using field crops returns. On the contrary, there is no evidence that ethanol can be used to forecast any region of the field crops distribution.
Keywords: C22; C53; Q13; Q42; Q47; 330; Biofuels; Ethanol; Field Crops; Density Forecasting; Granger Causality; Quantiles