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Integrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.
Integrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.
Value at risk (VaR) is a quantitative measure used to evaluate the risk linked to the potential loss of investment or capital. Estimation of the VaR entails the quantification of prospective losses in a portfolio of investments, using a certain likelihood, under normal market conditions within a specific time period. The objective of this article is to construct a model and estimate the VaR for a diversified portfolio consisting of multiple cash commodity positions driven by standard Brownian motions and jump processes. Subsequently, a thorough analytical estimation of the VaR is conducted for the proposed model. The results are then applied to two distinct commodities—corn and soybean—enabling a comprehensive comparison of the VaR values in the presence and absence of jumps.
PurposeThis study’s purpose is to analyze the effects of trade interventions and non-tariff impediments between the exporters (the United States and Brazil) and China for soybean trade.Design/methodology/approachA spatial model is developed and solved using an optimized Monte Carlo simulation (OMCS) and is used to minimize the costs of supplying soybeans to China. The costs included the origin basis; transportation to ports, including trucks, railways and barges; demurrage; and ocean freight. The sum of these charges determines the delivered costs to China from each origin. Most variables are random and correlated. Time-series distributions are based on historical data. Production and exports are highly seasonal and important.FindingsBase-case flows are highly seasonal, are risky and reflect actual trade. Sensitivities illustrate the effects of mitigating the quality differentials and interpreting a term of the Phase One agreement that purchases would be made so long as the prices are competitive. The results are also used to illustrate the influence of diversifying from the United States as a supplier. Finally, the policy implications are discussed.Research limitations/implicationsRemoving the quality discounts for US soybeans raises the US market share by 9%. These results also illustrate that diversification of supply sources is important for the importing country. Indeed, if China were to pursue less diversification import costs and/or risks would escalate. Hence, these results suggest that diversification is an appealing element of an import strategy. The results suggest a large distribution of prices and costs, particularly in Brazil. On average, the United States is most likely to be competitive for only a few months of the year, and the results are highly seasonal.Practical implicationsCompetition in supplying soybean to China is extremely competitive and the underlying factors impacting spatial competition are risk, correlated and spatially dependent. In addition to these, there are quality differences, and there are trade policies and strategies that affect competition. The empirical model and results illustrate the intensity of competition in this market as well the impacts of these non-tariff barriers and trade strategies in this market.Social implicationsImportant policies have been taken and continue to be under review regarding competition and trade among these countries. These results illustrate the impacts of these policies on market shares and competition.Originality/valueThis problem is important to the world soybean trading sector, and the methodology captures important seasonal and random variables that affect trade flows. The OMCS model is appropriate for this problem and has only been used minimally in the recent literature about commodity trade.
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