Exchange rates have long been thought to have an important impact on the export and import of goods and services, and, thus, exchange rates are expected to influence the price of those products that are traded. At the same time, energy impacts commodity production in some very important ways. The use of chemical and petroleum derived inputs has increased in agriculture over time; the prices of these critical inputs, then, would be expected to alter supply, and, therefore, the prices of commodities using these inputs. Also, agricultural commodities have been increasingly used to produce energy, thereby leading to an expectation of a linkage between energy and commodity markets. In this paper, we examine the price relationship through time of the primary agricultural commodities, exchange rates, and oil prices. Using overlapping time periods, we examine the cointegration relationship between prices to determine changes in the strength of the linkage between markets through time. In general, we find that commodity prices are linked to oil for corn, cotton, and soybeans, but not for wheat, and that exchange rates do play a role in the linkage of prices over time.

This article focuses on the effect of differing heteroscedasticity assumptions on derived premium rates of area‐yield crop insurance. Tests of the proportional and absolute heteroscedasticity assumptions are conducted using both in‐sample and out‐of‐sample measures. Our results suggest that arbitrarily imposing a specific form of heteroscedasticity or homoscedasticity in insurance rate calculations limits actuarial soundness. Our results have practical implications for the federal crop insurance programs, as we reject the traditional rating assumptions for many cotton regions and lower‐yielding/higher‐risk corn and soybean counties but not in the heart of the Cornbelt.

This article proposes the use of moment functions and maximum entropy techniques as a flexible approach for estimating conditional crop yield distributions. We present a moment‐based model that extends previous approaches, and is easily estimated using standard econometric estimators. Predicted moments under alternative regimes are used as constraints in a maximum entropy framework to analyze the distributional impacts of switching regimes. An empirical application for Arkansas, Mississippi, and Texas upland cotton demonstrates how climate and irrigation affect the shape of the yield distribution, and allows us to illustrate several advantages of our moment‐based maximum entropy approach.

This study revisits the large but inconclusive body of research on crop yield distributions. Using competing techniques across 3,852 crop/county combinations we can reconcile some inconsistencies in previous studies. We examine linear, polynomial, and ARIMA trend models. Normality tests are undertaken, with an implementable R-test and multivariate testing to account for spatial correlation. Empirical results show limited support for stochastic trends in yields. Results also show that normality rejection rates depend on the trend specification. Corn Belt corn and soybeans yields are negatively skewed while they tend to become more normal as one moves away from the Corn Belt.A gricultural economists have long investigated yield uncertainty because this fundamental attribute of agricultural production profoundly affects economic decisions such as crop acreage allocations, input use, and crop insurance demand and rate making. While this vein of literature is extensive, it has failed to produce a consensus regarding the appropriate approach to crop yield distribution specification.Several studies have rejected the assumption of normally distributed crop yields. In addition, some studies conclude that crop yields are positively skewed while other studies conclude that they are negatively skewed. Because these studies use a wide variety of data, normality tests, and detrending techniques, one may conjecture that the inconsistency of results is caused by the techniques used.

Hedge funds exhibit performance persistence if some funds have consistently higher returns than others. Several procedures are used to determine if performance persists. The results show that performance persists in hedge funds with some funds showing the greatest persistence across all procedures. The results also indicate a strong negative relation between hedge fund capitalization and returns, which is consistent with the hypothesis that hedge fund managers exploit market inefficiencies.

Rating insurance policies depends on the probability of events in the tail of a distribution. A method to measure such tail‐related risk based on Extreme Value Theory could potentially improve insurance rating. It is also widely agreed that there is a spatial structure to crop yield distributions. Considering the spatial structure may provide more precisely rated policies. In this context, this research provides two contributions in rating area yield crop insurance. One is to provide a method that fits the tail of crop yield distributions using the Generalized Pareto Distribution (GPD), a member of the family of extreme value distributions that models only the tail of the distribution. The second is to estimate parameters of the distribution using a Bayesian Kriging approach that provides spatial smoothing of GPD parameters. The proposed model provides estimates of the spatial structure across regions such as the maximum distance of the spatial effect. Based on an out‐of‐sample performance game between a private insurance company and the federal agency the proposed model provides considerable improvement, particularly when rating deeper tail probability.

Despite the potential benefits of larger datasets for crop insurance ratings, pooling yields with similar distributions is not a common practice. The current USDA-RMA county insurance ratings do not consider information across state lines, a politically driven assumption that ignores a wealth of climate and agronomic evidence suggesting that growing regions are not constrained by state boundaries. We test the appropriateness of this assumption, and provide empirical grounds for benefits of pooling datasets.We find evidence in favor of pooling across state lines, with poolable counties sometimes being as far as 2,500 miles apart. An out-of-sample performance exercise suggests our proposed pooling framework out-performs a no-pooling alternative, and supports the hypothesis that economic losses should be expected as a result of not adopting our pooling framework. Our findings have strong empirical and policy implications for accurate modeling of yield distributions and vis-à-vis the rating of crop insurance products.

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