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This study utilizes unique product barcode, store, and retail real estate data to calculate consistent estimates of the effects of retail market structure on food prices in the US. Our uniquely disaggregated data allow for identification strategy that corrects for the type of endogeneity that plagues many previous studies on price-concentration relationship. Empirical findings from an instrumental-variables fixed-effects model indicate that retail concentration is endogenous to price determination. Further, retail prices are found to rise with retail concentration. Importantly, ignoring endogeneity results in a severe downward bias in the estimated effects of concentration on food prices.

This study provides an empirical investigation of the relationship between grocery retail concentration and retail dairy prices in the US. We perform the analysis based on a unique dataset on store-level retail prices provided by the Information Resources Inc. Further, we consider alternative measures of retail concentration including revenue and store selling space-based Herfindahl Hirschman Index, which we compute using a Nielsen TDLinx dataset on store characteristics. Results from a reduced-from empirical framework estimated via panel data techniques indicate that grocery retail concentration affects dairy retail prices favorably. This central result is robust to the way that retail concentration is measured and is consistent with the empirical evidence from both the US and oversees.

This article contributes to the debate on time series properties of commodity cash and futures markets and the impact of speculation on commodity futures markets. We reconcile production theory, which predicts cash commodity prices will be mean-reverting, with the efficient market hypothesis which is consistent with unit root process for futures prices. It is shown that when the underlying cash price series does not contain a unit root, a nearby futures price series can be nonlinear, having martingale properties within each contract segment, and mean-reverting changes at contract rollover points. We develop a novel ECM-BEKK-MEX model that handles nonlinearities in futures prices in a simple and practical way, and allows full flexibility in modeling the impact of speculation on conditional cash and futures price variances while preserving positive definiteness of the bivariate variance matrix. The model is applied to the U.S. dairy sector.

The Margin Protection Program for Dairy Producers, created under the Agricultural Act of 2014, introduces a new margin insurance program that pays dairy producers when national incomeover- feed-cost margin declines below elected coverage level. A potential side effect of the program is that it may crowd out hedging using CME dairy futures and options. We analyze this issue under the assumption that hedging and Margin Protection Program are utilized by dairy producers as a protection against catastrophic margin risks. We model such behavior using safety-first preferences where farmers minimize hedge ratio subject to probabilistic constraint on revenue falling below a critical threshold level. We use Monte Carlo simulations and accounting data on dairy cost of production in two US regions to compare the crowding-out effect on representative producers in the upper and lower Midwest. We find that the magnitude of the crowding out depends on production efficiency, market risk exposure, and the timing of the Margin Protection Program sign-up.

In the period leading up to the start of the Margin Protection Program for Dairy Producers (MPP-Dairy), a survey was undertaken to assess dairy farmer knowledge, attitudes, impressions and expected participation decisions. All surveys were collected in July and August 2014. There are six main conclusions of the survey. First, prior to the announcement of USDA rules regarding MPP-Dairy, most dairy producers felt they had ‘some knowledge’ of the program, with close to 30 percent declaring ‘no knowledge’ about the program. Second, about thirty percent of respondents had somewhat or very favorable impressions of MPP-Dairy while similar percent had somewhat or very unfavorable. Top four concerns about the program were too much government involvement, program complexity, lack of supply management and fear that the program would distort market signals to farmers. Third, close to 40 percent of producers indicated they were leaning towards registering for MPP-Dairy, while 30 percent were leaning against participation. Over 30 percent of producers had not decided if they would to participate or not. More knowledge about MPP-Dairy was associated with higher likelihood of participation. Fourth, we asked dairy farmers which coverage level they would choose in most years. Distribution of responses had two pronounced peaks, with $4.00/cwt and $6.00/cwt chosen by 28 and 25 percent of producers respectively. No other coverage choice was chosen by more than 12 percent of producers. Fifth, just under 70 percent of LGM-Dairy users indicated that when faced with a one-time irreversible choice between LGM-Dairy and MPP-Dairy, they would choose MPP-Dairy. Finally, just under 25 percent of producers indicated MPP-Dairy would ‘somewhat’ reduce their use of other risk management tools, while 18 percent replied they expected a ‘strong reduction’ in their use of other risk management instruments.

We develop a new parametric bootstrap-based statistical test for presence of futures price and options-based implied volatility biases. The new test is applicable to data with overlapping prediction horizons. Information on anticipated volatility embedded in options prices is explicitly used when testing for futures price biases. Our method is well adapted to analysis of fast changing commodity markets as it does not rely on asymptotic theory and does not require a time series spanning several decades. We apply the new test to investigate if futures and options biases can explain very low loss ratios exhibited by USDA’s Livestock Gross Margin for Dairy Cattle insurance program.

Milk production occurs in every state and the highly perishable nature of the product means that every state also has some processing capacity. It is ubiquitous. Dairy farming and all of the downstream activity and economics also is sufficiently different from other forms of agriculture that dairy has evolved its own set of institutions and policies. Perhaps it is not surprising then that the issues and problems of the dairy sector over the years present their own story and have their own academic literature. Inelastic demand and low short-term price responsiveness of milk supply make dairy markets very volatile. Macroeconomic shocks, biofuels policy and increased exposure to international trade, have combined to make dairy profit margin shocks particularly detrimental in the last decade. The policy response came in the form of the Agricultural Act of 2014 which introduced the Margin Protection Program for Dairy Producers and enabled California to join the Federal Milk Marketing Order system while keeping the state dairy quota system. These developments provoked intense research interest among U.S. agricultural economists in recent years, and this special issue of the Journal of Agribusiness brings together five articles on these topics. Before overviewing the contributed articles, this introduction takes a step back and situates the most recent policy innovations in a much broader historical context. Our hope is that by understanding the long-term trends in dairy markets and policy, we can better appreciate the present situation, and provide guidance for suggested research topics that should be addressed in the following years.

Price endogeneity has been ignored in previous analyses of food demand in China. We examine agricultural input price data from the China National Bureau of Statistics and use reduced-form price equations to account for price endogeneity in this setting. Applying our unique econometric approach to the analysis of provincial-level food demand in China, we find strong statistical evidence of price endogeneity. Models that ignore price endogeneity result in substantially biased elasticities and misleading estimates of future food demand in China.

Price endogeneity has been ignored in previous analyses of food demand in China. We examine agricultural input price data from the China National Bureau of Statistics and use reduced-form price equations to account for price endogeneity in this setting. Applying our unique econometric approach to the analysis of provincial-level food demand in China, we find strong statistical evidence of price endogeneity. Models that ignore price endogeneity result in substantially biased elasticities and misleading estimates of future food demand in China.

In this analysis we compare the total expected government outlays and distribution of benefits under newly proposed dairy margin insurance programs to those under existing counter-cyclical payment programs. We combine simulation and structural modeling techniques to forecast milk price and dairy income-over-feed-cost margins. Using the price forecasts we employ Monte-Carlo experiments to evaluate the total expected government outlays for a sample of 5000 representative farms given a constant relative risk aversion utility framework. We find that expected outlays favor large farm operations and are an order of magnitude higher than those under existing programs. Under the current policy framework (MILC), farms with less than 100 cows (76% of farms) account for 42% of net payments and farms over 1000 cows (2% of farms) account for 6% of net payments. Under the new policy regime farms with fewer than 100 cows will get 17-21% of net program benefits, and farms over 1000 cows will get 36-43% of benefits.

The article offers measures of own and cross cost pass-through in a structural framework. Unlike the traditional reduced-form analysis used in the previous literature, our approach is applicable in situations where firm marginal cost data are unobserved. The empirical value of the model is illustrated in an application to retail pass-through for national and store brand yogurt using an inverse demand derived from the economic theory. We find that ignoring cross pass-through between the brands biases own pass-through estimates.

The endogeneity of retail market power arises in the retail pricing equation due to the correlation between margins and unobserved cost components. Nevertheless, it has long been ignored in the equilibrium analysis of retail behavior. We address the issue via a control function approach in a new conceptual framework with consumer preferences represented by a benefit function. We further offer three test procedures to evaluate the endogeneity of retail market power. The empirical value of the model is illustrated in an application to the US yogurt industry. Outcomes from endogeneity tests provide strong evidence for market power endogeneity. Moreover, ignoring the issue results in downward bias in retail market power.

A common approach in the literature, whether the investigation is about futures price risk premiums or biases in option-based implied volatility coefficients, is to use samples in which consecutive observations can be regarded as uncorrelated. That will be the case for non- overlapping forecast horizons constructed by either focusing on short time-to-maturity contracts or excluding some data. In this article we propose a parametric bootstrap procedure for uncovering futures and options biases in data characterized by overlapping horizons and correlated prediction errors. We apply our method to test hypotheses that futures prices are efficient and unbiased predictors of terminal prices, and that squared implied volatility, multiplied by time left to option expiry, is an unbiased predictor of terminal log-price variance. We apply the test to corn, soybean meal and Class III milk futures and options data for the period 2000-2011. We find evidence for downward bias in soybean meal futures, as well as downward volatility bias in Class III milk options. Importance of these results is illustrated on the example of premium determination for Livestock Gross Margin Insurance for Dairy Cattle (LGM-Dairy).

With increased volatility of feed prices dairy farm managers are no longer concerned with managing just milk price volatility but are considering the adoption of risk management programs that address income over feed cost (IOFC) margin risk. Successful margin risk management should be founded on understanding of the behavior of IOFC margins. To that end, we construct forward IOFC margins using Class III milk, corn and soybean meal futures prices. We focus on the characteristics of the term structure of forward IOFC margins, i.e. the sequence of forward margins for consecutive calendar months, all observed on the same trading day. What is apparent from shapes of these term structures is that both in times when margins were exceptionally high and when they were disastrously low, market participants expected that a reversal back to average margin levels would not come quickly, but would rather take up to 9 months. Slopes of the forward margin term structure prior to and after most of the major swings in IOFC indicate these shocks were mostly unanticipated, while time needed for recovery to normal margin levels was successfully predicted. This suggests that IOFC margins may exhibit slow mean-reverting, rather than predictable cyclical behavior, as is often suggested in the popular press. This finding can be exploited to design a successful catastrophic risk management program by initiating protection at 9 to 12 months prior to futures contract maturity. As a case study, we analyze risk management strategies for managing IOFC margins that utilize Livestock Gross Margin for Dairy Cattle (LGM-Dairy) insurance contracts. We created two farm profiles where the first one represents dairy farms that grow most of their feed. The second profile is designed to capture the risk exposure of a dairy farm that purchases all their dairy herd, dry cow, and heifer feed. Our case study of this program encompasses the 2009 period which was characterized by exceptionally poor IOFC margin conditions. We analyzed the dynamics of realized IOFC margins in 2009 under four different risk management strategies, and found that optimal strategies that are founded on principles delineated above succeeded in reducing the decline IOFC margins in 2009 by 93% for home-feed and 47% for market-feed profile, and performed substantially better than alternative strategies suggested by earlier literature.

A critical issue in understanding relationships between cash and futures markets is identifying the relevant and comparable cash and futures prices used in the analysis. In dairy markets, a problem arises from the simple fact that there exists no daily cash market for milk. The focal dairy cash market is the spot market for cheddar cheese at the Chicago Mercantile Exchange, while cheese futures contracts were only recently developed. In this article, we use a deterministic relationship between regulated prices for Class III milk and wholesale prices for cheese, dry whey, and butter to develop synthetic cheese futures that approximate cheese futures prices since 2000. Based on the measured accuracy of the synthetic cheese futures, we suggest this series can be paired with cash cheese prices and used in analysis of price dynamics between cash and futures dairy markets.

Livestock Gross Margin Insurance for Dairy Cattle (LGM-Dairy) is a recently introduced tool for protecting average income over feed cost margins in milk production. In this paper we examine the assumptions underpinning the rating method used to determine premiums charged for LGM-Dairy insurance contracts. The first test relates to the assumption of lognormality in terminal futures prices. Using high-frequency futures and options data for milk, corn and soybean meal we estimate implied densities with flexible higher moments. Simulations indicate there is no strong evidence that imposing lognormality introduces bias in LGM-Dairy premiums. The remainder of the paper is dedicated to examining dependency between milk and feed marginal distributions. The current LGM-Dairy rating method imposes the restriction of zero conditional correlation between milk and corn, as well as milk and soybean meal futures prices. Using futures data from 1998-2011 we find that allowing for non-zero milk-feed correlations considerably reduces LGM-Dairy premiums for insurance contracts with substantial declared feed amounts. Further examination of the nature of milk-feed dependencies reveals that Spearman’s correlation coefficient is mostly reflecting tail dependence. Using the empirical copula approach we find that non-parametric method of modeling milk-feed dependence decreases LGM-Dairy premiums more than a method that allows only for linear correlation. Unlike other situations in portfolio risk assessment where extremal dependence increases risk, in agricultural margins, tail dependence between feed and the Class III milk price may actually decrease insurance risk, and reduce actuarially fair premiums.

We investigate price discovery, volatility spillovers and impacts of speculation in the dairy sector. Examining the time series properties of cheese cash and implied futures price we find that the unit root hypothesis is strongly rejected for cash prices, while unit roots cannot be rejected for nearby futures prices in the framework that carefully controls for rollovers. To explain this result, we built a model that illustrates the time series properties of the nearby futures price series for a futures contract written on a second-order stationary cash series and identified the mean-reverting nonlinear dynamics that will occur at rollovers. Given the time series properties of the cash and futures series we propose an error-correction model using spreads between cash and the second nearby futures instead of the cointegration vector. To account for volatility dynamics we propose an extension of the BEKK variance model that we refer to as GARCH-MEX. That model does not restrict the sign of the additional regressors on the conditional variances, and can easily insure positive-definiteness of the conditional covariance matrix. We find that the flow of information in the mean model is predominantly from futures to cash, while volatility spillovers are bidirectional. It is possible that cash prices that include unfilled bid/offers react differently to increases in volatility in futures prices than sales cash prices, indicating that liquidity in the cash market is reduced with increase in conditional volatility of the futures price. Utilizing GARCH-MEX model we find strong evidence against the speculation on realized volatility of futures prices, bid-ask spread and magnitude of slippage.

Options on agricultural futures are popular financial instruments used for agricultural price risk management and to speculate on future price movements. Poor performance of Black’s classical option pricing model has stimulated many researchers to introduce pricing models that are more consistent with observed option premiums. However, most models are motivated solely from the standpoint of the time series properties of futures prices and need for improvements in forecasting and hedging performance. In this paper we propose a novel arbitrage pricing model motivated from the economic theory of optimal storage, and consistent with implications of plant physiology on the importance of weather stress. We introduce a pricing model for options on futures based on a Generalized Lambda Distribution (GLD) that allows greater flexibility in higher moments of the expected terminal distribution of futures price. We use times and sales data for corn futures and options for the period 1995-2009 to estimate the implied skewness parameter separately for each trading day. An economic explanation is then presented for inter-year variations in implied skewness based on the theory of storage. After controlling for changes in planned acreage, we find a statistically significant negative relationship between ending stocks-to-use and implied skewness, as predicted by the theory of storage. Furthermore, intra-year dynamics of implied skewness reflect the fact that resolution of uncertainty in corn supply is resolved between late June and middle of October, i.e. during corn growth phases that encompass corn silking through grain maturity. Impacts of storage and weather on the distribution of terminal futures price jointly explain upward sloping implied volatility curves.

Ph.D. Thesis advisor: Dr. T.Randall Fortenbery, University of Wisconsin-Madison [See http://marinbozic.info/research.php for other publications by this author]

Options on agricultural futures are popular financial instruments used for agricultural price risk management and to speculate on future price movements. Poor performance of Black’s classical option pricing model has stimulated many researchers to introduce pricing models that are more consistent with observed option premiums. However, most models are motivated solely from the standpoint of the time series properties of futures prices and need for improvements in forecasting and hedging performance. In this paper we propose a novel arbitrage pricing model motivated from the economic theory of optimal storage. We introduce a pricing model for options on futures based on a Generalized Lambda Distribution (GLD) that allows greater flexibility in higher moments of the expected terminal distribution of futures price. We show how to use high-frequency data to estimate implied skewness and kurtosis parameters. We propose an economic explanation for variations in skewness based on the theory of storage. We use times and sales data for corn futures and options on futures for the period 1995-2009. After controlling for changes in planned acreage, we find a statistically significant negative relationships between ending stocks-to-use and implied skewness and kurtosis, as predicted by the theory of storage.

Utilizing realized prediction errors we can graphically show the forecasting performance of futures prices over an extended time period. Using this method we can easily identify time‐to‐maturity and seasonality components of risk premiums. Figures developed reveal nonlinear behavior of forecasting bias in the time‐to‐maturity dimension, and the importance of information revealed at the end of June for seasonal pattern of bias. In further research we will develop statistical tests for futures prices unbiasedness based on correlated realized prediction errors.

In this paper we have analyzed dynamics of milk prices in U.S. and performance of futures contracts as a tool to hedge price risk. Using spectral analysis, we find that lowering of support prices in early 1990s has induced 3-year business cycle with rising amplitude. We analyzed correlations of dairy prices and found current set of active dairy futures contracts to contain several redundant contracts. We utilized a new tool to visualize risk premium in futures prices to examine if Class III futures prices are unbiased predictors of announced USDA Class III price and we found no substantial evidence of bias. Employing partially overlapping time series model of Smith (2005) we analyzed seasonality and sources of volatility in futures prices. Model results indicate that futures prices volatility is higher in summer months, increases as time-to-maturity decreases up until eight weeks to maturity when settlement price starts to become increasingly predictable and uncertainty is quickly resolved. We name this decline in volatility in the last two trading months Inverse Samuelson effect. Further results reveal that common factor explains 50-60% of contract variance, with highest fraction of variance explained by common contract being 80+% about six to nine months prior to expiry when information regarding upper bound of dairy herd size at the time of maturity is revealed. Importance of common factor has itself pronounced seasonality that corresponds to seasonal cycles in dairy cow culling., Replaced with revised version of paper 02/10/10.

Advisors: Prof. Giovanni Pegoretti (Italy) and Prof. Brian W. Gould (USA) ... [See http://marinbozic.info/research.php for other publications by this author.]

There is continuing pressure by various farm groups to attempt to solve the chronic problems in the U.S. dairy industry represented by increased milk price variability, inability to generate positive returns at the farm level, increasing role of dairy exports as an important market for U.S. dairy products, etc. As such it is important for analysts and policy makers obtain an estimate as to how responsive dairy producers are to changing economic and technological conditions. Examples of previous research used to examine supply response in the U.S. dairy sector include LaFrance and deGorter (1985), Chavas and Klemme (1986), Thraen and Hammond (1987), Chavas, Krauss and Jesse (1990), Chavas and Krauss (1990), Yavuz, et al, (1996) and USDA (2007). These analyses are limited in that either they are either fairly dated or they do not account the dynamics that are inherent in the dairy herd expansion/contraction process. The above overview of the dairy industry points to a changing industry as represented by reduced but larger dairy operations, the changing nature of U.S. dairy policy and pricing, production of new types of dairy products, etc. with much of the adjustments have occurred since the above previous analyses were undertaken and may no longer reflect the industries supply characteristics. The present study will incorporate data encompassing the 1975-2007 period and provide an update of the model original developed by Chavas and Klemme (1986). This study has three main objectives: (i) quantify the current supply structure of the U.S. dairy industry, (ii) gain insight into impacts of technological changes that have occurred over the last 25 years, (iii) based on (i) and (ii), generate forecasts of long-run milk supply response to price changes and possible future technological advancements.

The endogeneity of retail markups arises due to the correlation between the markups and unobserved costs in the retail pricing equation. This correlations may be a result unobserved product quality affecting both price and markups. Despite inconsistency resulting from markup endogeneity, it has long been ignored in the equilibrium analysis of retail behavior. We account for retail markup endogeneity using a control-function approach in which controls are derived from empirical evidence in the marketing literature. Furthermore, we adopt three test procedures to evaluate this endogeneity and apply our method in an econometric analysis of retail market behavior in the marketing of yogurt in the United States. The results provide strong statistical evidence for the fact that markup endogeneity has been overlooked, resulting in upward bias in retail markups.