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The purpose of this paper is to derive a new option pricing model for options on futures calendar spreads. Calendar spread option volume has been low and a more precise model to price them could lead to lower bid-ask spreads as well as more accurate marking to market of open positions.

The new option pricing model is a two-factor model with the futures price and the convenience yield as the two factors. The key assumption is that convenience follows arithmetic Brownian motion. The new model and alternative models are tested using corn futures prices. The testing considers both the accuracy of distributional assumptions and the accuracy of the models’ predictions of historical payoffs.

Panel unit root tests fail to reject the unit root null hypothesis for historical calendar spreads and thus they support the assumption of convenience yield following arithmetic Brownian motion. Option payoffs are estimated with five different models and the relative performance of the models is determined using bias and root mean squared error. The new model outperforms the four other models; most of the other models overestimate actual payoffs.

The model is parameterized using historical data due to data limitations although future research could consider implied parameters. The model assumes that storage costs are constant and so it cannot separate between negative convenience yield and mismeasured storage costs.

The over 30-year search for a calendar spread pricing model has not produced a satisfactory model. Current models that do not assume cointegration will overprice calendar spread options. The model used by the Chicago Mercantile Exchange for marking to market of open positions is shown to work poorly. The model proposed here could be used as a basis for automated trading on calendar spread options as well as marking to market of open positions.

The model is new. The empirical work supports both the model’s assumptions and its predictions as being more accurate than competing models.

The purpose of this paper is to estimate crop yield densities considering time trends in the first three moments and spatially varying coefficients.

Yield density parameters are assumed to be spatially correlated, through a Gaussian spatial process. This study spatially smooth multiple parameters using Bayesian Kriging.

Assuming that county yields follow skew normal distributions, the location parameter increased faster in the eastern and northwestern counties of Iowa, while the scale increased faster in southern counties and the shape parameter increased more (implying less left skewness) in southwestern counties. Over time, the mean has increased sharply, while the variance and left skewness increased modestly.

Bayesian Kriging can smooth time-varying yield distributions, handle unbalanced panel data and provide estimates when data are missing. Most past models used a two-stage estimation procedure, while our procedure estimates parameters jointly.

The authors determine the effectiveness of the Rainfall Index Annual Forage Program (RIAFP) in offsetting yield risk of winter annual forage growers. The authors also evaluate the effectiveness in reducing risk of potential alternative weather indices.

The RIAFP is designed to compensate forage producers when yield losses occur. Prior research found weak correlation between the rainfall index and actual winter annual forage yields. The authors use long-term small-plot variety trials of rye, ryegrass, wheat, triticale and oats with rainfall recorded on site and measure the correlation of the index with actual rainfall and actual yields. The alternative indices include frequency of precipitation events and of days with temperature below freezing.

The correlation between actual rainfall and the current RMA index was strongly positive as in previous research. Correlations between forage yields and monthly intervals of the current RMA index were mostly statistically insignificant, and many had an unexpected sign. All indices had some correlations that were inconsistent across time intervals and forage variety. The inconsistent signs suggest a nonlinear relationship with weather and forage yield, indicating that rainfall can be too much or too little. The number of days below freezing has the most potential of the three measures examined.

Producers should view the winter forage RIAFP as a risk-increasing income-transfer farm program. A product to reduce the risk for forage producers may need to use a crop growth simulation model or another approach that can capture the nonlinearity.

Considerably more data were considered than in past research. Past research did not consider alternative weather indices. The program should be continued if its goal is to serve as disguised income transfer, but it should be discontinued if its goal is to reduce risk.

The purpose of this study was to determine if a preference for round prices exists in the wheat market and how wheat sales react to price movements around whole‐dollar amounts. The results show round prices are slightly more prevalent than non round prices, and transactions increase when price moves above a whole‐dollar amount. While such predictable behavior could be exploited by speculators in other markets, the effect is not large enough to merit concern in the market studied here.

The purpose of this paper is to develop an option pricing model applicable to US options. The lognormality assumption that has typically been imposed with past binomial and trinomial option pricing models is relaxed. The relaxed lattice model is then used to determine skewness and kurtosis of distributions of futures prices implied from option prices.

The relaxed lattice is based on Gaussian quadrature. The markets studied include corn, soybeans, and wheat. Skewness and kurtosis are implied by minimizing the squared deviations of actual option premia from predicted premia.

Positive skewness is the major source of nonnormality, but both skewness and kurtosis are important as the trinomial model that considers kurtosis has greater accuracy than the binomial model. The out‐of‐sample forecasting accuracy of the relaxed lattice models is better than the Black‐Scholes model in most, but not all cases.

The model might benefit from using option prices from more than one day. The implied skewness and kurtosis were quite variable and using more data might reduce this variability.

Empirical results mostly show positive implied skewness, which suggests extreme price rises were more likely than extreme price decreases.

The relaxed lattice is a new model and the results about implied higher moments are new for these commodities. There are competing models available that should be able to get similar accuracy, so one key advantage of the new approach is its simplicity and ease of use.

Consistent and reliable data on farmland values is critical to assessing the overall financial health of agricultural producers. However, little is known about the idiosyncrasies and similarities of standard land value data sources – US Department of Agriculture (USDA), Federal Reserve Bank land value surveys, and transaction prices. The purpose of this paper is to determine the differences and similarities of land value movements from three land value data sources.

In addition to Oklahoma transaction prices, two survey sources are considered: the USDA annual report and the quarterly Tenth District Survey of Agricultural Credit Conditions administered by the Federal Reserve Bank of Kansas City. The paper describes each data set and identifies differences in data sampling, collection, and reporting. Average values of Oklahoma farmland across data sources are examined. USDA estimates are regressed against quarterly Federal Reserve values across multiple states to determine the point in time represented by USDA estimates. Granger causality tests determine if Federal Reserve land value estimates anticipate movements in USDA land value estimates.

It is found that all three data sources are highly correlated, but transaction prices tend to be higher, especially for irrigated cropland and ranchland. USDA land values are reported as representing land values on January first, but instead they more closely represent first and second quarter land values according to a multi‐state comparison to changes in quarterly Federal Reserve land values. Given the finding that first quarter Federal Reserve Bank land values lead USDA land values and that they are published before the USDA release, Federal Reserve land values are a timely indicator of agricultural producers' financial position.

No previous research has addressed the topic of how various sources of agricultural land values compare.

This paper aims to determine the effects of agricultural, recreational and urban variables on Oklahoma land prices.

An econometric model is estimated using price of agricultural land parcels as the dependent variable and independent variables representing agricultural, recreational and urban uses. Recreational variables include county‐level recreational income from Agricultural Census data as well as deer harvest for each county. Urban variables are functions of population and income for each county. The agricultural variables include rainfall as well as crop returns for cropland and cattle prices for pasture.

Agricultural variables are the most important, followed by urban and then recreational variables. Transaction prices are higher than commonly used land‐value survey data. The major recreational variable is deer harvest, which is more important in small tracts. The value of pasture is now greater than cropland. Small tract sizes receive substantial premiums.

Agriculture is still an important part of the Oklahoma economy, so the findings might differ in more densely populated states. As with most econometric models, there are possible biases due to errors in measurement or missing explanatory variables.

The paper provides information that could be used by those wanting to estimate land value or wanting to manage land to increase its value.

The paper differs from previous work in both variables considered and the data used. Also, most previous work has not as directly addressed the issue of the relative importance of agricultural, recreational and urban variables.

The purpose of this paper is to use prediction markets to forecast an agricultural event: United States Department of Agriculture's number of cattle on feed (COF). Prediction markets are increasingly popular forecast tools due to their flexibility and proven accuracy to forecast a diverse array of events.

During spring 2008, a market was constructed comprised of student traders in which they bought and sold contracts whose value was contingent on the number of COF to be reported on April 18, 2008. During a nine‐week period, students were presented three types of contracts to forecast the number of COF. To estimate forecasts a uniform price sealed bid auction mechanism was used.

The results showed that prediction markets forecasted 11.5 million head on feed, which was about 1.6 percent lower than the actual number of COF (11.684 million). The prediction market also fared slightly worse than analysts' predictions, which on average suggested there would be about 11.795 million head (an over‐estimate of about 1 percent).

The contribution of this study was not to provide conclusive evidence on the efficacy of using prediction markets to forecast COF, but rather to present an empirical example that will spark interest among agricultural economists on the promises and pitfalls of a research method that has been relatively underutilized in the agricultural economics literature.

This paper aims to provide empirical insight into the trends and structural changes in meat consumption in Vietnam.

This study applies the Quadratic Almost Ideal Demand System model on multiple cross-sectional data sets derived from the Vietnam Household Living Standards Survey (VHLSS) of 2004–2016 and follows a consistent two-step procedure to deal with the zero consumption issue. The estimated demand elasticities are then compared and analyzed over time.

The empirical results show that in the last decade, meat consumption patterns in Vietnam have undergone a remarkable structural change, with poultry and beef increasingly taking important roles in the meat consumption structure of households. In addition, demographic characteristics, including settlement type, household size and the age of the household head, have significantly influenced meat expenditure patterns in Vietnam.

The paper considers the demand for meat consumed at home but not meat consumed away from home.

In many developing countries, increased disposable income, together with rapid urbanization and international integration, has significantly changed consumers' food consumption behaviors. This is one of the first studies using household survey data, which permits the exploration of heterogeneous preferences between consumers, to explore structural changes in food consumption patterns in Vietnam.

This study aims to spot wheat data and disaggregated commitment of trader data for CME traded wheat futures to examine the effect of exogenous shocks for hedging positions of Producers and Swap Dealers on cash-futures basis and excess futures returns.

A Bayesian vector autoregression (BVAR) methodology is used to capture volatility transfer effects.

Evidence is presented that institutional short hedging positions play a major role in the pricing of asymmetric information held by Swap Dealers into the basis. The results also indicate that producer hedging contains information when conditions in the supply chain create a shift in long vs short hedging demand. Finally, the results demonstrate that that Swap Dealer short hedging has the greatest effect on risk premium size and historical volatility.

Various proxies for spot prices are used in the literature, although actual spot price data is not common. In addition, stationarity for basis and open interest data is induced using the Baxter-King filter which allows us to work with levels, rather than percentage changes, in the time series data. This provides the ability to directly observe the effect of outright open interest positions for hedgers on contemporaneous innovations in basis and in excess returns. The use of a BVAR methodology represents an improvement over other structural VAR models by capturing contemporaneous systemic effects within an endogeneity based structural framework.