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Vedenov, Dm, .
Texas A&M University
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Problem Statement:Agricultural producers and processors dealing with multiple commodities (e.g. soybean crushers, cattle feeders, etc.) are subject to price risk on both the input and output side. Cross-commodity price dependence affects the efficiency of risk management strategies that involve multiple instruments (a combination of futures, insurance, and options). Producers need to have tools to quickly evaluate the risk-reducing effectiveness of various strategies and select the ones best suited to their particular risk exposure. Research is also needed to provide a deeper understanding of how commodity price behavior over time affects the performance of hedging strategies. Enhancing knowledge in these areas would enhance the economic conditions of agricultural producers and improve their competitiveness in the global market.Justification:Due to commodity price fluctuations over time, agricultural producers dealing with multiple commodities are subject to price risk on both input and output sides. Furthermore, since the processing cycle may take several weeks, the unfavorable price movements between the purchase of the inputs and the sale of the output(s) are of particular concern. The risk management tools available to the producers include futures, options, and insurance programs. Hedging with futures is typically preferred, since it is less costly than buying options or insurance.In the multi-commodity setting faced, the performance of hedging strategies is determined not just by the price risk in each separate market, but also by the interaction between these sources of risk. Analysis of multi-commodity hedging has been mostly performed under the variance minimization (MV) criterion (e.g. Fackler & McNew, 1993; Peterson & Leuthold, 1987). In recent years, minimization of downside risk measures, such as the lower partial moment (LPM2), has been commonly used as a criterion in determining the optimal hedging strategies (e.g. Mattos et al., 2008; Power & Vedenov, 2010).Cross-commodity price interaction substantially affects the performance of hedging strategies and needs to be taken into account in order to improve hedging efficiency. Literature in this area has been focusing primarily on energy markets (e.g. Haigh and Holt, 2002; Liu, Vedenov and Power, 2017). However, limited work in this area has been done for agricultural commodities.Furthermore, expansion of trading in commodity futures to financial holding companies in 2003-2011 led to financialization of and consequent changes in the commodity markets particularly in the aftermath of the financial crisis of 2008. The changing behavior of commodity prices and dependence between those create a need to evaluate the traditional hedging strategies and develop new alternatives better suited to the current market conditions.Changing behavior of commodity prices also affects interaction between various risk management instruments, such as futures, options, and insurance. Combining several risk instruments can reduce the cost and improve the effectiveness of the overall risk management strategy (e.g. Power, Vedenov, and Hong, 2009). Development of such multi-instrument strategies and analyzing their effectiveness under the changing price behavior is an important research direction which can provide producers with improved risk management options.The analysis of the issues outlined above calls for a combination of a traditional econometric approach and innovative stochastic simulation modeling techniques, such as use of copulas to model joint distributions of spot and futures prices.Objectives:Analyze the structural changes in the commodity market behavior since 2008.Analyze effectiveness of multi-commodity hedging strategies for specific agricultural production activities and measure the impact of changes in commodity markets on those.Develop multi-instrument risk management policies and analyze their effectiveness.Provide recommendations to the producers on best ways to adapt to changing market conditions so as to enhance their economic stability and improve competitiveness in the global markets.
Funding Source
Nat'l. Inst. of Food and Agriculture
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Risk Assessment, Management, and Communication