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The use of futures contracts has grown enormously in recent years. From 1979 to 1985 the number of futures contracts traded literally doubled. Most of the growth can be attributed to the development of recent contract innovations. Trading in financial futures, alone, increased sixteen fold over this period. This remarkable rise in futures usage and the importance of innovation highlights the constant struggle by exchanges to develop and initiate successful contracts. However, there is no known process for actually identifying potentially successful contracts. lt is this general question of what leads to a successful contract that forms the initiative behind this work. Formally, this study is a theoretical and empirical analysis of futures usage. The purpose of the theoretical section is to develop a model of contract usage that leads to a set of testable hypotheses about the determinants of contract use. Usage is defined in this study as being measured by the number of contracts in existence at a certain time. The theoretical work is general in the sense that it is not directed at behavior in one specific contract; but rather, it rests on the belief that certain underlying fundamental economic factors will affect, in general, usage in all futures contracts. The theoretical model is based upon firm behavior in an uncertain world with the firm having the ability to enter a portfolio of futures contracts. The purpose of the empirical section is to provide support for the theoretical section by determining, through time series analysis, which fundamental variables affect futures usage and how these effects are transmitted. The exogenous variables center upon the variance-covariance matrix of actual price series, transactions costs, and production levels. The empirical results yield strong support for the theoretical section developed in this work and the overall portfolio approach. Additionally, the results draw into question the importance of several variables which have classically been considered essential in determining usage. While the results strongly support the model and the portfolio perspective, they did not suggest a specific set of variables that uniquely determine contract usage across a wide set of different contracts. / Ph. D. / incomplete_metadata

Thesis (Ph. D.)--University of Wisconsin, Milwaukee, 2005. / Vita. Includes bibliographical references (leaves 64-68).

Thesis: M.S., Massachusetts Institute of Technology, Sloan School of Management, 1978 / Includes bibliographical references. / M.S. / M.S. Massachusetts Institute of Technology, Sloan School of Management

Li, Xu. / Thesis submitted in: December 2008. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2009. / Includes bibliographical references (leaves 41-43). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.ii / Chapter 1 --- Introduction --- p.1 / Chapter 2 --- Background Study --- p.6 / Chapter 3 --- Model Formulation --- p.8 / Chapter 3.1 --- Trading Cost Function --- p.9 / Chapter 3.2 --- Notations and Optimality Equation --- p.11 / Chapter 4 --- Optimal Policy --- p.14 / Chapter 4.1 --- Preliminary Assumption and Results --- p.14 / Chapter 4.1.1 --- "Generalized (s, 5, H) Policy" --- p.14 / Chapter 4.1.2 --- Polya Distribution and Quasi-K-convex --- p.15 / Chapter 4.1.3 --- Assumptions --- p.20 / Chapter 4.2 --- Single Period Problem --- p.23 / Chapter 4.3 --- Finite-Period Problem --- p.30 / Chapter 4.4 --- The Algorithm --- p.36 / Chapter 5 --- Conclusion --- p.39 / Bibliography --- p.41

Historical evidence has shown that increases in coffee prices, though generating a short term gain in export earnings for producers, also lead to increased plantings of the coffee crop. This in turn leads to overproduction and a subsequent drop in coffee prices. The establishment of the International Coffee Agreements was meant to stabilize this fluctuating behaviour in coffee prices. / The purpose of this paper is to present an overall analysis of coffee prices in order to predict the future course of prices under two circumstances. First, when the international coffee market is governed by the mandates of the International Coffee Agreements and secondly, when the market operates under free market conditions. The paper also attempts to draw parallels between the structure and mechanisms of the markets of coffee and tea. The first part of this paper provides a background study of the markets of coffee and tea including a discussion of the commodity cartels which have been signed to date in both markets. Part two provides a historical analysis of prices in both markets, followed by an econometric analysis of the demand for coffee in the largest consuming country in the world, the United States.

Thesis (L.L.M.)--University of Hong Kong, 1991. / Xerox opy of typescript.

Thesis (L.L.M.)--University of Hong Kong, 1991. / Also available in print.

In several countries around the world, grain commodities are traded as assets on stock exchanges. This indicate that the market and effectively the prices of the grain commodities in such countries, are controlled by several local and international economic, political and social factors that are rapidly changing. As a result, the prices of some grain commodities are volatile and trading in such commodities are prone to price-related risks. There are different trading strategies for minimising price-related risks and maximising profits. But empirical research suggests that making the right decision for effective grain commodities trading has been a difficult task for stakeholders due to high volatility of grain commodities prices. Studies have shown that this is more challenging among grain commodities farmers because of their lack of skills and the time to sift through and make sense of the datasets on the plethora of factors that influence the grain commodities market. This thesis focused on providing an answer for the main research problem that grain farmers in South Africa do not take full advantage of all the available strategies for trading their grain commodities because of the complexities associated with monitoring the large datasets that influence the grain commodities market. The main objective set by this study is to design a framework that can be followed to collect, integrate and analyse datasets that influence trading decisions of grain farmers in South Africa about grain commodities. This study takes advantage of the developments in Big Data and Data Science to achieve the set objective using the Design Science Research (DSR) methodology. The prediction of future prices of grain commodities for the different trading strategies was identified as an important factor for making better decisions when trading grain commodities and the key factors that influence the prices were identified. This was followed by a critical review of the literature to determine how the concepts of Big Data and Data Science can be leveraged for an effective grain commodities trading decision support. This resulted in a proposed framework for grain commodities trading. The proposed framework suggested an investigation of the factors that influence the prices of grain commodities as the basis for acquiring the relevant datasets. The proposed framework suggested the adoption of the Big Data approach in acquiring, preparing and integrating relevant datasets from several sources. Furthermore, it was suggested that algorithmic models for predicting grain commodities prices can be developed on top of the data layer of the proposed framework to provide real-time decision support. The proposed framework suggests the need for a carefully designed visualisation of the result and the collected data that promotes user experience. Lastly, the proposed framework included a technology consideration component to support the Big Data and Data Science approach of the framework. To demonstrate that the proposed framework addressed the main problem of this research, datasets from several sources on trading white maize in South Africa and the factors that influence market were streamed, integrated and analysed. Backpropagation Neural Network algorithm was used for modelling the prices of white maize for spot and futures trading strategies were predicted. There are other modelling techniques such as the Box-Jenkins statistical time series analysis methodology. But, Neural Networks was identified as more suitable for time series data with complex patterns and relationships. A demonstration system was setup to provide effective decision support by using near real-time data to provide a dynamic predictive analytics for the spot and December futures contract prices of white maize in South Africa. Comparative analysis of predictions made using the model from the proposed framework to actual data indicated a significant degree of accuracy. A further evaluation was carried out by asking experienced traders to make predictions for the spot and December futures contract prices of white maize. The result of the exercise indicated that the predictions from the developed model were much closer to the actual prices. This indicated that the proposed framework is technically capable and generally useful. It also shows that the proposed framework can be used to provide decision support about trading grain commodities to stakeholders with lesser skills, experience and resources. The practical contribution of this thesis is that relevant datasets from several sources can be streamed into an integrated data source in real-time, which can be used as input for a real-time learning algorithmic model for predicting grain commodities prices. This will make it possible for a predictive analytics that responds to market volatility thereby providing an effective decision support for grain commodities trading. Another practical contribution of this thesis is a proposed framework that can be followed for developing a Decision Support System for trading in grain commodities. This thesis made theoretical contributions by building on the information processing theory and the decision making theory. The theoretical contribution of this thesis consists of the identification of Big Data approach, tools and techniques for eradicating uncertainty and equivocality in grain commodities trading decision making process.

This thesis examines the relationship between spot prices, futures prices, and ending stocks for storable commodities. We used Granger causality and DAGs to determine causal relationships and cointegration tests to determine long-run relationships. We use VAR/VECM and consider innovation accounting techniques to see how volatility in one market affects the price behavior and volatility in the other market. Results suggest that for agricultural commodities, innovations in futures price permanently increase the level of spot prices while accounting for much of spot price variance over time. For national oil, shocks to futures price decrease the level of spot price in the long run. In regional oil markets, there are transitory impulse responses. Futures price plays a small role in the volatility of spot prices for oil over time. Overall results are mixed, with oil suggesting futures markets may have a price stabilizing effect and agriculture commodities indicating spot price destabilization.