This paper examines the value-at-risk (VaR) implications of mean-variance hedging. We derive an equivalence between the VaR-based hedge and the mean-variance hedging. This method transfers the investor's subjective risk-aversion coefficient into the estimated VaR measure. As a result, we characterize the collapse probability bounds under which the VaR-based hedge could be insignificantly different from the minimum-variance hedge in the presence of estimation risk. The results indicate that the squared information ratio of futures returns is the primary factor determining the difference between the minimum-variance and VaR-based hedges.

The Pasture, Rangeland, Forage (PRF) insurance program is aimed to assist producers to manage the risk of forage loss due to the lack of precipitation. However, limited attention has been given to understanding the implications of policyholders’ coverage selection decisions. In this study, three alternative risk-efficient portfolio selection strategies are assessed in to the context of the PRF program. Proposed methods consider all the decision parameters and program restrictions, and they highlighted the underlying relationships between expected revenue, risk, and choice of the coverage parameters. Selection strategies are illustrated by examining the optimal coverage for a grid in South Texas.

The marginal benefit and cost of diversification for Florida orange producers is analyzed using certainty equivalents. Results indicate that for moderate and high levels of risk aversion, diversification into strawberry, grapefruit, or additional orange production is not optimal. However, moderately risk averse Florida orange producers can gain by diversifying into grapefruit production if the annual amortized fixed costs can be reduced by as little as 10 percent.

E-V studies traditionally have relied on historical data to calculate returns and variance. Historical data may not fully reflect current conditions, particularly when decisions involve government-supported crops. This paper presents a method for calculating mean and variance using subjectively-estimated data. The method is developed for both government-supported and non-program crops. Comparisons to alternative methods suggest the approach provides reasonable accuracy.

In-person interviews were carried out with Mexican millers who were administered a conjoint-type survey designed to incorporate uncertainty in attribute levels. Two methods were used to model millers' risk preferences: a modified mean-variance approach and an explicit expected utility approach. Controlling for variability, Mexican millers are willing to pay premiums for increases in quality factors such as test weight, protein content, falling number, and dough strength/extensibility. We find millers are not particularly sensitive to changes in the variability of quality characteristics. Out-of-sample forecasts suggest the mean-variance model provides an accurate depiction of actual Mexican imports.

A whole farm economic analysis was conducted to provide a detailed assessment into the economic, risk, and production implications due to the adoption of auto-steer navigation. It was determined that auto-steer navigation was profitable for a grain farmer in Kentucky with net returns increasing up to 0.90% ($3.35/acre). Additionally, the technology could be used in reducing production risk. Adoption of the technology also alters production practices for optimal use.