Crop Insurance Valuation under Alternative Yield Distributions

Considerable disagreement exists about the most appropriate characterization of farm-level yield distributions. Yet, the economic importance of alternative yield distribution specifications on crop insurance valuation has not been well documented. The results of this study demonstrate that large differences in expected payouts from popular crop insurance products can arise solely from the parameterization chosen to represent yield distributions. The results suggest that the frequently unexamined yield distribution specification may lead to economically significant errors in crop insurance policy rating and assessment of expected payouts from policies.     

Speculation and corn prices

It is commonly asserted that speculative trading activities are largely behind the high and volatile food commodity price behaviour since 2006. In this article, we revisit this hypothesis by investigating how different speculative measures affect our conclusion on the role of speculation. Four speculative measures are considered, including index trading activities, non-commercial net long positions, Working’s speculative index, and an excessive speculative volume index. These four measures imply different underlying hypotheses about the role of speculation on commodity price movements and encompass most of the measures used in the recent literature on the role of speculation in commodity markets. Using a structural vector autoregressive (SVAR) model, we show a mixed impact of speculative trading on corn prices depending on the measure used. While shocks to index trading activities and excess speculation as measured by Working’s T have either zero or negative impact on corn prices, a positive shock to non-commercial net positions or the Tadesse index significantly increases the price of corn. However, the magnitude of the impact is not large, at most about $0.30 per bushel in real terms. Our findings are robust to structural breaks, alternative ordering of variables, and an alternative specification of the model.     

A speculative bubble in commodity futures prices? Cross‐sectional evidence

Recent accusations against speculators in general and long-only commodity index funds in particular include: increasing market volatility, distorting historical price relationships, and fueling a rapid increase and decrease in the level of commodity prices. Some researchers have argued that these market participants—through their impact on market prices—may have inadvertently prevented the efficient distribution of food aid to deserving groups. Certainly, this result—if substantiated—would counter the classical argument that speculators make prices more efficient and thus improve the economic efficiency of the food marketing system. Given the very important policy implications, it is crucial to develop a more thorough understanding of long-only index funds and their potential market impact. Here, we review the criticisms (and rebuttals) levied against (and for) commodity index funds in recent U.S. Congressional testimonies. Then, additional empirical evidence is added regarding cross-sectional market returns and the relative levels of long-only index fund participation in 12 commodity futures markets. The empirical results provide scant evidence that long-only index funds impact returns across commodity futures markets.     

New Evidence on the Impact of Index Funds in U.S. Grain Futures Markets

Commodity index trader position data are examined for the years prior to the 2007–08 commodity price increase. New data from 2004 to 2005 show that a large increase in commodity index positions occurred in select grain futures markets. However, the increased index participation took place well in advance of the 2007–08 boom in prices. Granger causality tests fail to find any causal link between commodity index activity and grain futures prices. Furthermore, there is little evidence of an index‐induced price bubble using long‐horizon regressions. Nous avons analysé les données sur les positions des opérateurs de marché au cours des années qui ont précédé la hausse des prix des denrées en 2007–08. Selon de nouvelles données pour la période 2004–05, une hausse substantielle des positions liées à l’indice des denrées est survenue sur des marchés de grain à terme sélectionnés. Toutefois, cette hausse des positions est survenue bien avant la montrée en flèche des cours en 2007–08. Le test de causalité de Granger n’a pas permis d’établir l’existence d’un lien de causalité entre l’activité liée à l’indice des denrées et les cours à terme des grains. De plus, les régressions pour processus à mémoire longue ne permettent pas de conclure à l’existence d’une bulle des prix induite par l’indice.     

Improving the accuracy of outlook price forecasts

This study investigates the predictive ability of outlook hog price forecasts released by Iowa State University relative to alternative time‐series and market forecasts. Under root mean squared error (RMSE), the futures market forecast is most accurate at the first and second horizon but less accurate than Iowa outlook and the other forecast methods at the third horizon. In terms of the individual time‐series models, some vector autoregressions (VARs) and Bayesian VARs flexible in specification and estimation and model averaging tend to perform better than Iowa outlook forecasts. Evidence from encompassing tests, more stringent tests of forecast performance, indicates that many price forecasts can add incremental information to the Iowa forecast. Simple combinations of these models and outlook forecasts are able to reduce forecast errors by economically significant levels. Overall, the results indicate that it is possible to provide more accurate forecasts than Iowa outlook at every horizon.     

Pigs in Space: Modeling the Spatial Structure of Hog Production in Traditional and Nontraditional Production Regions

We posit a spatially explicit, county-level model of the hog production sector and estimate how numerous firm-specific, locality-specific, and spatial agglomeration factors affect the location, movement, and intensity of hog production within 15 key hog production states. Spatial agglomeration, urban encroachment, input availability, firm productivity, local economy, slaughter access, and regulatory stringency variables affect the sample regions' spatial organization. Analyses suggest that western states in the sample may shape hog production levels by wielding traditional business recruitment and retention tools (e.g., tax rates, environmental stringency) while Corn Belt states may shape hog production via nontraditional tools (e.g., land use controls).     

INNOVATION, PRICE COMPETITION AND THE 'TILT' OF COMPETITION POLICY

Innovation is the key to rising living standards; competition is the key to a rapid rate of innovation.