Master of Science / Department of Agricultural Economics / Ted C. Schroeder / Risk management decision makers face significant price risk when purchasing or selling wholesale beef. Previous research has identified cross-hedging wholesale beef in Live Cattle futures as a plausible means of reducing this risk. Changes in the way beef is marketed have led to poor performance of cross-hedging programs. Unlike earlier research, more recent studies have shown that Live Cattle futures are a poor venue for effective cross-hedging. This study replicates previous research to evaluate the current state of traditional cross-hedging performance. Focus then shifts to improving cross-hedging methods. Hedge ratios derived from a traditional cross-hedging methodology exhibit a great deal of sensitivity to season, estimation technique, and quality grade. Basis risk is abundant for this type of cross-hedging. To reduce the basis risk inherent with cross-hedging wholesale beef, bundling is proposed. This involves combining two or more cuts together in a single unit to be cross-hedged. Firms merchandising meat from a whole carcass would be able to provide a valuable risk management service if the basis risk faced when hedging a bundled product is less than the basis risk faced when cross-hedging the corresponding products independently. This research found that bundling has neither a positive or negative effect on basis risk. Therefore bundling is a plausible practice, but will not offer reduced basis risk to decision makers.

Doctor of Philosophy / Department of Agricultural Economics / Ted C. Schroeder / Risk is an inevitable part of agricultural production and all producers face various forms of risk. Output price has been shown to be the major contributor to the risk in cattle feeding, yet few choose to manage this risk. This study used subjective price expectations and price distributions of survey participants to determine how producer's expectations compare with that of the market. In addition, demographic information gathered from survey participants allowed for further examination as to how these factors effect price outlook and variability. Data used for this study were gathered through survey responses from Kansas State University Extension meeting and workshop participants and other meetings targeted to livestock producers. First, data were aggregated and analyzed at a group level. Only two of the twelve price forecast were significantly lower than the futures settlement price. On the other hand, all but one of the aggregated group volatility expectations was different. Typically nearby contract price risk expectation was underestimated and distant contract price risk expectation was overestimated. Individual respondent's discreet stated price and price distribution information was fitted to a continuous distribution and an implied mean and standard deviation were determined. These were compared to market price and price risk data. Respondent's expectation of price was significantly lower than the market for distant months for five of the six groups. Individual volatilities resulting from each fitted distribution were significantly lower from the volatility measure resulting from Black's model. Demographic data were estimated to show the impact of this information on overall error of price forecast and price risk expectations. Those living outside the Northeast and Northern Plains tended to have larger error in their expectation of price volatility. Larger backgrounding operations reported lower price variance error and selling more fed cattle each year increased price risk expectation error. Lastly, prior use of risk management tools for the most part did not have an impact on error in either price expectation or price volatility expectation.

Virginia's regional grain prices exhibit high volatility due to the state's unique spatial variability and supply and demand fundamentals. This study explains Virginia's basis patterns for corn, soybeans, and wheat. Discussion of times when there were outstanding economic or fundamental market movers that affected basis in Virginia is also offered. The feasibility and process of hedging using futures is explained and evaluated in a case study. Possible marketing decisions based on the findings are presented in an easy and accessible manner, so that producers and extension agents can use this information to make real-time grain marketing decisions. / Master of Science / Virginia's regional grain prices exhibit high volatility due to the state's unique spatial variability and supply and demand fundamentals. This study explains Virginia's basis patterns for corn, soybeans, and wheat. Discussion of times when there were outstanding economic or fundamental market movers that affected basis in Virginia is also offered. The feasibility and process of hedging using futures is explained and evaluated in a case study. Possible marketing decisions based on the findings are presented in an easy and accessible manner, so that producers and extension agents can use this information to make real-time grain marketing decisions.

This dissertation consists of three essays. In the first essay, econometric models are used to measure price risk in a study for major grains (wheat, rice, corn, and soybeans) in China. Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models and Multiplicative Heteroskedasticity (M Het) models are applied to estimate time-varying price variance, and then covariances are estimated by a simple two-step process assuming constant conditional correlations. An aggregate price risk index is constructed from these variances and covariances using an economic index number approach. In theory, this approach is superior to the more common approach of estimating a univariate GARCH model for an aggregate price index. This easay compares the two approaches to measuring aggregate price risk and finds low correlations. Thus there is substantial difference between the two approaches in practice as well as in theory. The previous essay measures aggregate price risk but does not explain price risk. The second essay attempts to investigate potential factors that contribute to aggregate price risk of major grain products (rice, wheat, corn and soybeans) on monthly base in China from mid 1980s to recent year from both theoretical and empirical perspectives. The superlative price risk indexes are explained by a set of key variables that characterize China’s economy, agricultural market and trade as well as biological system of major grain in China. These variables account for much of the variation in the aggregate price risk index. Moreover empirical results favor use of the superlative index of aggregate risk rather than standard measures of aggregate risk. The third essay is an extension of previous two essays by explaining price risk at disaggregate level. Price variances and covariances are modeled using both Ordinary Least Squares (OLS) and Seemly Unrelated Regression (SUR) techniques. Results are broadly consistent with the previous essays.

The study focuses on tariff-linked (or commodity-linked) agreements entered into between a power utility and commodity producers. The main purpose of these types of agreements is to link electricity tariff payable by commodity producers to the price of the commodity produced thereby transferring a certain level of commodity price risk to the power utility. The study looks at risk management practices of a power utility company with a particular reference to tariff-linked agreements. Also, the study critically analyses risk hedging mechanisms put in place by the power utility. The report makes practical recommendations, where applicable, in dealing with these risks. Risk management continuously evolve to meet the challenges of complex financial world. Despite the latest sophisticated risk management tools available commodity producers still encounter difficulties to hedge the price risk. The challenge for the power utility is the application of new risk management tools to effectively manage price risk. / Business Management / M.Com. (Business Economics)

The study focuses on tariff-linked (or commodity-linked) agreements entered into between a power utility and commodity producers. The main purpose of these types of agreements is to link electricity tariff payable by commodity producers to the price of the commodity produced thereby transferring a certain level of commodity price risk to the power utility. The study looks at risk management practices of a power utility company with a particular reference to tariff-linked agreements. Also, the study critically analyses risk hedging mechanisms put in place by the power utility. The report makes practical recommendations, where applicable, in dealing with these risks. Risk management continuously evolve to meet the challenges of complex financial world. Despite the latest sophisticated risk management tools available commodity producers still encounter difficulties to hedge the price risk. The challenge for the power utility is the application of new risk management tools to effectively manage price risk. / Business Management / M.Com. (Business Economics)

This study examines dynamic hedge policy of a company in a multi-period framework. The company begins to operate a project for a customer and it also has a subcontractor which completes an important part of the project by using an economic commodity. The customer will pay a fixed price to the company at the end of the project. Meanwhile, the company needs to pay the debt to the subcontractor and the amount of the debt depends on the spot price of the commodity at that time. The company is allowed to hedge for the commodity price fluctuations via future and option contracts. Since the company has a limited cash reserve as well as previously planned payments, it may face financial distress when the net cash balance decreases below zero. Consequently, the company maximizes the expected value of itself by minimizing the expected financial distress cost.

Kentucky plays a vital role in the beef supply chain. The cow/calf producers,back-grounding operations, and order buying industry are important parts of Kentucky'sagricultural economy. Basis risk is an issue that affects these groups in a negative way. Agood estimate of the expected basis must be available to make hedging efficient.Simulations were performed on Kentucky price data to determine the effectiveness ofshort hedging for Kentucky producers. A model was also used to describe some of thefactors that determine basis levels. The research revealed that it is difficult to predictbasis within an acceptable range to make short hedging with futures efficient. Eventhough short hedging reduced variability in net price, it was difficult to lock in a profit.Various options and spread strategies were presented as alternative hedging tools thatwould protect cattle producers from unexpected price declines.

Master of Science / Department of Agricultural Economics / Kevin C. Dhuyvetter / Risk is an inevitable part of production agriculture. Price risk is especially a concern for cattle producers in the Midwest. Producers can curtail profit volatility, to an extent, through the utilization of price risk management strategies such as forward contracting, hedging, using put and call options, Livestock Risk Protection Insurance (LRP), as well as Livestock Gross Insurance (LGM) for feedlot cattle. Learning about such price risk management tools can be a daunting task. Kansas State University Extension created a computer based simulation workshop to assist them in teaching cattle producers about price risk management strategies. The simulation paralleled a lecture where participants learned of the price risk management strategies that are available. The simulation allowed the workshop participants to practice using the management strategies as they assumed the role of a feedlot or ranch manager in charge of marketing the operation's calves. In a cooperative effort with Iowa State University, Kansas State University presented the Cattle Risk Management Workshops across the state of Iowa. Participants were given pre-and posttests to measure the effectiveness of the workshop. The overall post-test scores were 25 percentage points higher than the pre-test scores. This research also discusses the interest and perceptions of cattle producers regarding price risk management strategies. The effectiveness of simulations as a teaching tool in helping producers learn about price risk management strategies is also reviewed. In addition, the various price risk management strategies available to producers, as well as seasonality of prices and basis are analyzed. This research also explains and estimates the LRP Feeder Cattle Basis Model. The LRP Feeder Cattle Basis Model was developed with the objective of assisting producers in forecasting LRP basis. The model was developed using similar methodology applied in the creation of a CME basis forecasting model developed by Kansas State University Extension and Custom Ag Solutions, Inc. The LRP Feeder Cattle Basis Model automatically adjusts for the LRP price adjustment factor applied to beef steer calves weighing less than 600 pounds, and beef heifers weighing 600-900 pounds. The LRP Feeder Cattle Basis Model explains 71.37 percent of the variation of LRP basis.

A expansão do milho no Brasil é decorrente da mudança espaço-temporal da produção, que cria uma nova perspectiva de análise para a relação entre os preços do milho e da soja. Nesse contexto, este estudo objetiva analisar o transbordamento de risco entre esses mercados, por meio de três ensaios independentes, porém complementares. No primeiro ensaio, realiza-se uma revisão de literatura sobre a disseminação de riscos, com foco no mercado agrícola. Como estratégia de pesquisa realiza-se um levantamento de estudos publicados sobre os termos transmissão, transbordamento e contágio, que são utilizados de forma intercambiável pela literatura e que sintetizam os desdobramentos recentes no tocante à disseminação ou propagação de riscos entre mercados. No segundo ensaio, pretende-se identificar o contágio de preços dos mercados físicos e futuros de soja para o mercado de milho, com a utilização dos modelos de correlação condicional dinâmica. Os resultados indicam tanto a influência da crise financeira global de 2008 na relação entre os preços desses grãos, como mudanças nos padrões de correlação condicional entre o milho e a soja, principalmente em períodos de adversidades climáticas. Por fim, os maiores patamares de correlação condicional ocorrem na relação entre os preços físicos desses produtos, demonstrando que os agentes utilizam o conteúdo informacional presente nos preços de commodities correlatas em sua avaliação de risco. No terceiro ensaio, o transbordamento de risco é avaliado em termos de mudanças nos preços relativos de milho e soja em seus mercados físicos domésticos e futuros internacionais. Para esse fim, utiliza-se o modelo markoviano para identificar as mudanças de regime na relação de preços entre essas commodities e seu efeito sobre a estratégia de hedge. Os resultados sugerem que o comportamento das razões de preços no mercado físico doméstico e no mercado futuro externo divergem a partir de 2012. As mudanças nos regimes de preço no mercado doméstico refletem com mais intensidade o comportamento sazonal das duas safras de milho cultivadas ao longo do ano, enquanto que no setor externo, estão mais associadas às adversidades climáticas. Em termos de estratégias de hegde, a B3 consegue captar melhor o rearranjo de preços no mercado doméstico, apresentando uma elevação da razão ótima de hegde no regime de baixa (preços de soja e milho estão mais próximos). Resultado similar é obtido na CME, quando a mudança de regime de preços ocorre na razão de preços futuros. Em síntese, os resultados desse estudo evidenciam que a gestão de risco de preços não se limita apenas aos fundamentos e características de um mercado específico. Destaca-se assim a importância de se identificar fatores exógenos, presentes ou identificáveis em commodities correlatas, aperfeiçoando a estratégia de gestão de risco de preços. / The expansion of corn in Brazil is due to the spatio-temporal change in production, which creates a new perspective analysis for the price relationship between corn and soybeans. This study aims to analyze the risk of spillover between corn and soybeans markets, through three independent but complementary essays. In the first essay, a literature review on the spread of risks is carried out, focusing on the agricultural market. As a research strategy, carried out a survey of published studies on the subjects - transmission, spillover and contagion - which are used interchangeably in the literature, and summarizing the recent developments regarding the risk propagation across markets. In the second essay, we intend to identify the contagion of prices of spot and futures soybean prices for the corn market, with the use of dynamic conditional correlation models. The results indicate both the influence of the global financial crisis of 2008 on the relationship between prices of these grains and changes in the conditional correlation patterns between corn and soybeans, especially in periods of climatic adversity. Finally, the highest levels of conditional correlation occur in the relationship between the spot prices of these products, demonstrating that in their risk assessment, the agents use the informational content present in the prices of related commodities. In the third essay, risk of spillover is assessed in terms of changes in relative prices of corn and soybeans in the spot and futures markets. For this purpose, the Markov model is used to identify the regime change in the price relationship between these commodities and their effect on the hedging strategy. The results suggest that, as of 2012, the behavior of the price ratios in the domestic spot market differs from the behavior of the price ratio in the external futures market. Changes in price regimes in the domestic market respond more intensely to seasonal behavior of the two corn crops grown throughout the year, while in the external market, they are more associated with climatic adversities. In terms of hedge strategies, the B3 commodity exchange can better capture the rearrangement of prices in the domestic market, presenting an elevation of the optimal hegde ratio in the low regime (soybean and corn prices are closer). A similar result is obtained in CME, when the change in price regime occurs in the ratio of future prices. In summary, the results of this study show that price risk management is not limited to the fundamentals and characteristics of a specific market. This highlights the importance of identifying exogenous factors present or identifiable in related commodities, improving the strategy of price risk management.