Volatility in Cereal Prices: Intra‐ Versus Inter‐annual Volatility

Intra‐annual (within crop year) price volatility and inter‐annual (between crop years) price volatility are measured for wheat, maize, rice, barley, oats and rye. A set of explanatory variables is used in a pooled regression to explain variations in these price volatilities. With low cereal stocks, supply (yield) shocks (defined here as volatilities, as for the price volatilities) mostly influence inter‐annual volatility while other influential factors are the crude oil price and exchange rate. Cereal demand and interest rate shocks combined with low stocks affect intra‐annual volatility, while other explanatory factors include exchange rate and crude oil price shocks. The derivatives market activity appears to have no significant effect on either intra‐ or inter‐annual volatility. In contrast, large cereal stocks and a well‐functioning international cereal market reduce the effects of shocks in the explanatory variables on both intra‐ and inter‐annual volatilities.     

Evaluation of USDA Interval Forecasts of Corn and Soybean Prices

USDA World Agricultural Supply and Demand Estimates (WASDE) price forecasts are published as an interval, but are typically analyzed as point estimates. Thus, all information about uncertainty imbedded in the forecast is ignored. The purpose of this article is to evaluate the accuracy of WASDE corn and soybean price forecasts using methodology suitable for testing judgmental interval forecasts. Accuracy tests suggest that WASDE forecasts are not calibrated at the 95% confidence level for both commodities and generally not calibrated for corn, but calibrated for soybeans, at the implied confidence level elicited from the survey of forecast providers.     

Developing Hedging Strategies for Québec Hog Producers under Revenue Insurance

The paper investigates the optimal hedging strategies of Québec hog producers when they participate in a publicly funded revenue insurance program known as ASRA (Régime d'assurance-stabilisation des revenus agricoles). A forecast model of local cash and futures prices is built and Monte Carlo methods are used to derive the optimal futures and option positions of Québec hog producers. The positive correlation between forecasts of futures and cash spot prices induces positive sales of futures and put options to hedge price risk. ASRA provides put options to hog producers at actuarially advantageous terms. Producers can increase the expected utility of profits by selling back a portion of these put options using financial markets. Options are attractive to manage price risk given the nonlinearity in the profit function induced by the revenue insurance scheme. Speculative incentives to use futures and options are also discussed in the context of ASRA.     

Martingale Restrictions and the Implied Market Price of Risk

The market price of risk is conceptually one of the most critical artifacts of modern finance, since it provides the linkage between equilibrium and arbitrage models of derivatives pricing. In this paper, the market price of risk is derived for options on live cattle futures contracts. It provides a technique to extract the implied market price of risk (iMPR), which is conceptually similar to that used in extracting implied volatilities. It is shown that the iMPR is not linear across strike prices as theory suggests it should.
Le prix de marché du risque est conceptuellement l'un des artéfacts les plus importants de la finance moderne puisqu'il établit le lien entre les modèles d'équilibre et les modèles d'évaluation par arbitrage de l'établissement des prix des dérivés. Dans le présent article, le prix de marché du risque est dérivé pour les options sur contrats à terme de bovins vivants. Il offre une technique pour extraire le prix de marché implicite du risque qui est conceptuellement similaire à celle utilisée pour extraire les volatilités implicites. Il est montré que le prix de marché implicite du risque n'est pas linéaire pour tous les prix de levée comme la théorie semble l'indiquer.     

Martingale Restrictions and the Implied Market Price of Risk

The market price of risk is conceptually one of the most critical artifacts of modern finance, since it provides the linkage between equilibrium and arbitrage models of derivatives pricing. In this paper, the market price of risk is derived for options on live cattle futures contracts. It provides a technique to extract the implied market price of risk (iMPR), which is conceptually similar to that used in extracting implied volatilities. It is shown that the iMPR is not linear across strike prices as theory suggests it should.
Le prix de marché du risque est conceptuellement l'un des artéfacts les plus importants de la finance moderne puisqu'il établit le lien entre les modèles d'équilibre et les modèles d'évaluation par arbitrage de l'établissement des prix des dérivés. Dans le présent article, le prix de marché du risque est dérivé pour les options sur contrats à terme de bovins vivants. Il offre une technique pour extraire le prix de marché implicite du risque qui est conceptuellement similaire à celle utilisée pour extraire les volatilités implicites. Il est montré que le prix de marché implicite du risque n'est pas linéaire pour tous les prix de levée comme la théorie semble l'indiquer.     

Predictive performance of the World Bank's commodity price projections

The World Bank's commodity price projections are widely used for various planning purposes. Two aspects of the Bank's projections of relative prices are studied in this paper. The first is whether the forecasts make efficient use of the information available at the time the forecast is made. The second is whether the forecasts predict future prices with greater accuracy than alternative forecasting methods. These matters are studied by comparing the World Bank's past price projections with the actual prices that were subsequently observed. The results show that, overall, the World Bank forecasts do not pass either test. First, the World Bank forecasts are informationally inefficient. Prediction error (projection minus actual price) tends to be positively correlated with the projections themselves. Although the direction of future price movements tends to be correctly predicted, the magnitude of these movements tends to be overpredicted. Second, the World Bank forecasts do not perform well even compared with the simplest of alternative forecasting methods - the prediction of no change.     

Valuing seasonal climate forecasts in a state‐contingent manner

We applied state‐contingent theory to climate uncertainty at a farm level to assess the value of seasonal climate forecasts in the Central West region of NSW. We find that modelling uncertainty in a state‐contingent manner results in a lower estimate of forecast value than the typical expected value approach. We attribute this finding to a more conservative long‐term farm plan in the discrete stochastic programming (DSP) model, which is better balanced for climate uncertainty. Hence, a climate forecast, even though it still revises probabilities held by farmers, does not call forth such large changes in farm plans and associated farm incomes. We then use the DSP model to assess how attributes of a hypothetical forecasting system, particularly its skill and timeliness, as well as attributes of the decision environment, influence its value. Lastly, we assess the value of current operational forecast systems and show that the value derived from seasonal climate forecasts is relatively limited in the case study region largely because of low skill embodied in forecasts at the time when major farm decisions are being made.     

Has the Market's Estimate of Crop Price Variability Increased since the 1996 Farm Bill?

Following enactment of the 1996 Farm Bill, corn and soybean implied volatilities covering the preharvest and storage seasons increased 16–23% between 1987–1995 and 1997–2001. The increase was statistically significant at the 90% confidence level. Standard deviation of corn and soybean prices derived from the implied volatilities increased 7–25%, but only the increase for preharvest corn was statistically significant. Further muddling the picture is the decline in variability of annual U.S. average corn and soybean cash price. These mixed findings point to continuing disagreement about government's role in managing farm risk in the post-1996 Farm Bill world.     

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.     

The role of the U.S. exchange-rate equity market volatility on agricultural exports and forecasts

This article estimates the U.S. state-level soybean export forecast until December 2024 using a seasonal autoregressive integrated moving average (SARIMA) model. We utilize the newly developed exchange-rate equity market volatility (EMV-EX) to improve model fit and the Dirichlet process mixture model (DPMM) to control for unobserved heterogeneity. Using monthly data from January 2004 to December 2020, the study shows that soybean exports for states without ports are underestimated at the expense of states with ports. The EMV-EX has a positive effect on soybean exports. The forecasts reveal no expected changes in the trends for soybean exports until December 2024. This study's results are useful to make and to implement more informed policy decisions for risk-mitigating strategies such as the market-facilitation program.     

Forecast performance of WASDE price projections for U.S. corn

We conduct a comprehensive evaluation of the season‐average price projections for U.S. corn as published by the U.S. Department of Agriculture's World Agricultural Supply and Demand Estimates (WASDE), an important issue given reduced resources and increased program scrutiny within the Federal Government. This study is the first in the literature to evaluate the WASDE corn projections relative to futures adjusted forecasts throughout the forecasting cycle using a lengthy evaluation period (1980/81–2012/13). We find that WASDE projections provide lower RMSEs relative to futures adjusted forecasts for 9 of the 16 forecast periods, 4 of which are statistically different. Encompassing tests show that WASDE projections often provide incremental information not present in the futures adjusted forecasts. Composite forecasts based on futures adjusted forecasts and WASDE projections reduced the RMSEs over all forecast periods by an average 12–16%. Favorable average trading profits may be generated for some forecast months using WASDE projections. Overall, our results suggest that WASDE projections of the U.S. corn season‐average price provide useful information to the market and could enhance the efficiency of the agricultural sector.     

FORECASTING FOR AUSTRALIAN AGRICULTURE

The paper provides a review of the objectives of forecasts and of the techniques for generating forecasts in the context of agriculture. Forecasts provide information to facilitate decision making. The techniques are evaluated in terms of assumptions about the processes generating the forecast variables, their relative requirements for time, data and other resources, and their relative forecast accuracy. An evaluation of naive, informal model and econometric model forecasts of Australian agricultural commodity prices and production levels is reported.     

Forecast Encompassing as the Necessary Condition to Reject Futures Market Efficiency: Fluid Milk Futures

The traditional necessary condition for futures market inefficiency is the existence of alternative forecasting methods that produce mean squared forecast errors smaller than the futures market. Here, a more exacting requirement for futures market efficiency is proposed—forecast encompassing. Using the procedure of Harvey and Newbold , multiple forecast encompassing is tested using Chicago Mercantile Exchange fluid milk futures. Time series models and USDA experts provide competing forecasts. Results suggest milk futures do not encompass the information contained in the USDA forecasts at a two-quarter horizon. While the competing forecasts generate positive revenues, it is unlikely that returns exceed transaction costs in this relatively new market.     

Do nonrenewable-energy prices affect renewable-energy volatility? The case of wood pellets

Over the past decade, the U.S. Southeast has experienced a rapid expansion of wood-pellet biomass production for European export. This renewable wood-pellet supply requires nonrenewable-energy inputs in its manufacturing and logistics, which suggests possible price-volatility spillovers between renewable and nonrenewable markets. A BEKK-MGARCH model is employed for investigating these possible price-volatility spillovers. Overall, results suggest a limited negative effect of past volatile nonrenewable-energy prices influencing current wood-pellet price volatility. Specifically, high volatilities in nonrenewable-energy prices do not affect the volatility of wood-pellet prices. Thus, any stability concerns in terms of nonrenewable-input prices affecting the wood-pellet market are not warranted.     

Using a functional approach to test trending volatility in the price of Mexican and international agricultural products

In this article, we extend the traditional GARCH(1,1) model by including a functional trend term in the conditional volatility of a time series. We derive the main properties of the model and apply it to all agricultural commodities in the Mexican CPI basket, as well as to the international prices of maize, wheat, swine, poultry, and beef products for three different time periods that implied changes in price regulations and behavior: before the North American Free Trade Agreement (NAFTA; 1987–1993), post‐NAFTA (1994–2005), and commodity supercycle (2006–2014). The proposed model seems to adequately fit the volatility process and, according to heteroscedasticity tests, also outperforms the ARCH(1) and GARCH(1,1) models, some of the most popular approaches used in the literature to analyze price volatility. Our results show that, consistent with anecdotal evidence, price volatility trends increased from the period 1987–1993 to 1994–2005. From 1994–2005 to 2006–2014, trends decreased but the persistence of volatility increased for most products, especially for international commodities. In addition, we identify some agricultural products such as avocado, beans, and chicken that, due to their increasing price volatility trends in the 2006–2014 period, may present a risk for food inflation in the short run.     

Information-rich wheat markets in the early days of COVID-19

This paper uses the information implicit in commodity futures and options prices to infer market beliefs about the impact of early-stages COVID-19 on commodity market fundamentals. The particular commodity examined is soft red winter (SRW) wheat, and the timeframe is early February to late March 2020. The analysis highlights various adjustments in the cash and futures price of SRW wheat in light of surging short-run demand from consumer hoarding of staple food products, and a weakening long-run market from growing wheat stocks and an emerging global recession. This split is causing the forward curve to flatten and basis levels to invert. The change over time in the price of options on wheat futures reveals increased price volatility in response to growing uncertainty about the COVID-19 impacts. Similarly, changes in the skewness of the option's volatility smile illustrate a shift in traders’ perception about risk in the right versus left tail of the price distribution.     

Are Revisions to USDA Crop Production Forecasts Smoothed?

This article uses Nordhaus' framework to determine the efficiency of the revision process for USDA corn and soybean production forecasts over the 1970/1971 through 2004/2005 marketing years. Positive autocorrelation and consistency of directional changes in forecast revisions suggest these forecasts are "smoothed." Evidence is provided that the loss in forecast accuracy due to smoothing is statistically and economically significant in several cases. A conservative bias in farm operators' assessments of yield potential and in the procedure for translating enumerator's information about plant fruit counts into objective yield estimates are identified as plausible sources of smoothing.     

Investing Today or Tomorrow? An Experimental Approach to Farmers’ Decision Behaviour

An understanding of farmers’ decision‐making behaviour is important for adequate forecasts as well as policy recommendations regarding structural changes. We experimentally analyse the investment behaviour of real farmers. The observed investment decisions are contrasted with theoretical benchmarks from classical investment theory and the real options approach. Our results show that both theories cannot exactly explain investment behaviour. However, farmers learn from former investment decisions and do consider the value of waiting over time.     

A systems approach to forecast agricultural land transformation and soil environmental risk from economic,policy, and cultural scenarios in the north central United States (2012–2062)

Grassland conversion to row-crop production in the north central United States has been a growing threat to socio-economic and environmental sustainability for producers, conservationists, and policy-makers alike. We used a system dynamics model of the region to forecast agriculturally driven land transformation through mid-twenty-first century. The base-case scenario projection showed that farmland area continued to increase, from under 200,000?km² to over 230,000?km². Unmitigated, the soil environmental risk (SER) of such changes reached conservative estimates of Dust Bowl-era externalities. Systems analyses show that reducing livestock production costs, doubling conservation compliance requirements, and livestock–cropping integration had the largest impact on grassland conservation and mitigating SER. The largest SER effects came from eliminating conservation incentives or raising cultivation incentives, despite improvements in reduced tillage and enhanced agronomy. Several system archetypes were identified within the policy scenarios: ‘fixes that backfire’ and ‘success-to-the-successful’. For scenarios creating favourable impacts, time delays caused some behaviours to worsen before positive gains were realized. If implemented, patience and persistence to ensure that these scenarios reach their full potential will be necessary. Our scenarios provide quantitative forecasts around measures for sustainable intensification. These projections can aid regional stakeholders in enhancing discussions currently taking place about sustainable agriculture in the region.     

Modeling regime‐dependent agricultural commodity price volatilities

In stark contrast to financial markets, relatively little attention has been given to modeling agricultural commodity price volatility. In recent years, numerous methodologies with various strengths have been proposed for modeling price volatility in financial markets. We propose using a mixture of normals with unique GARCH processes in each component for modeling agricultural commodity prices. While a normal mixture model is quite flexible and allows for time varying skewness and kurtosis, its biggest strength is that each component can be viewed as a different market regime and thus estimated parameters are more readily interpreted. We apply the proposed model to ten different agricultural commodity weekly cash prices. Both in‐sample fit and out‐of‐sample forecasting tests confirm that the two‐state NM‐GARCH approach performs better than the traditional normal GARCH model. A significant and state‐dependent inverse leverage effect is detected only for pork in the regime where the price is expected to drop, indicating the volatility in this regime tends to increase more following a realized price rise than a realized price drop.