This dissertation contains three essays. In the first essay I use a volatility spillover model to find evidence of significant spillovers from crude oil prices to corn cash and futures prices, and that these spillover effects are time-varying. Results reveal that corn markets have become much more connected to crude oil markets after the introduction of the Energy Policy Act of 2005. Furthermore, crude oil prices transmit positive volatility spillovers into corn prices and movements in corn prices become more energy-driven as the ethanol gasoline consumption ratio increases. Based on this strong volatility link between crude oil and corn prices, a new cross hedging strategy for managing corn price risk using oil futures is examined and its performance studied. Results show that this cross hedging strategy provides only slightly better hedging performance compared to traditional hedging in corn futures markets alone. The implication is that hedging corn price risk in corn futures markets alone can still provide relatively satisfactory performance in the biofuel era.The second essay studies the spillover effect of biofuel policy on participation in the Conservation Reserve Program. Landowners' participation decisions are modeled using a real options framework. A novel aspect of the model is that it captures the structural change in agriculture caused by rising biofuel production. The resulting model is used to simulate the spillover effect under various conditions. In particular, I simulate how increased growth in agricultural returns, persistence of the biofuel production boom, and the volatility surrounding agricultural returns, affect conservation program participation decisions. Policy implications of these results are also discussed. The third essay proposes a methodology to construct a risk-adjusted implied volatility measure that removes the forecasting bias of the model-free implied volatility measure. The risk adjustment is based on a closed-form relationship between the expectation of future volatility and the model-free implied volatility assuming a jump-diffusion model. I use a GMM estimation framework to identify the key model parameters needed to apply the model. An empirical application to corn futures implied volatility is used to illustrate the methodology and demonstrate differences between my approach and the model-free implied volatility using observed corn option prices. I compare the risk-adjusted forecast with the unadjusted forecast as well as other alternatives; and results suggest that the risk-adjusted volatility is unbiased, informationally more efficient, and has superior predictive power over the alternatives considered.
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