The interconnections of agriculture and energy markets have increased through the rise in the new biofuel agribusinesses and the oil-ethanol-corn linkages. The question is whether these linkages have a causal structure by which oil prices affect commodity prices and through these links, instability is transferred from energy markets to already volatile agricultural markets. In this article, we present empirical results using contemporary time-series analysis and Granger causality supplemented by a directed graph theory modeling approach to identify the links and plausible contemporaneous causal structures among energy and commodity variables. The results show that although there is a strong correlation among oil and commodity prices, the evidence for a causal link from oil to commodity prices is mixed.

This article's focus is on the time adjustment paths of the exchange rate and prices in response to unanticipated monetary shocks. First, we expand the theoretical specification of the overshooting hypothesis by generalizing Dornbusch's model to include a third sector (i.e., agricultural prices). Second, we employ Johansen's cointegration test along with a vector error correction model to investigate whether agricultural prices overshoot in an open economy. The empirical results indicate that agricultural prices adjust faster than industrial prices to innovations in the money supply, affecting relative prices in the short run, but strict long-run money neutrality does not hold. Copyright 2002, Oxford University Press.

Links between agricultural commodity and energy prices have become more complex with increased ethanol production. The concerns are whether the new corn-ethanol links lead to volatility-spillover effects between food and energy prices and different data-frequencies is the reason for previous inconsistent results. We investigate the asymmetric volatility-spillover effects between U.S. feedstock and biofuel prices, using an asymmetric BEKK-multivariate-GARCH approach, with daily, weekly, and monthly futures-price frequencies. The results show asymmetric volatility-spillover effects between corn and ethanol prices, and the volatility of corn and ethanol returns respond differently to positive and negative shocks, demonstrating asymmetric volatility transmission, depending on different data-frequencies.