Mitigating Hydrological Risk with Energy Derivatives |
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| Affiliation: | 1. Technology Management, Economics, and Policy Program, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 151–742, South Korea;2. Center for Transportation Research,Cockerel School of Engineering, The University of Texas at Austin, 1 University Station C1761, Austin, TX 78712, United States;3. Department of Energy Resources Engineering, Inha University, 100 Inharo, Nam-gu, Incheon 402–751, South Korea;1. West Virginia University, Division of Resource Economics and Management, Regional Research Institute, United States;2. Federal University of Juiz de Fora, Department of Economics, Brazil;3. University of São Paulo, Department of Economics, Brazil;1. College of Management and Economics, Tianjin University, Tianjin 300072, China;2. School of Economics and Finance, Xi''an Jiaotong University, Xi''an, Shaanxi 710049, China;3. Center for Energy & Environmental Policy Research, Institute of Science and Development, Chinese Academy of Sciences, Beijing 100190, China;4. Harvard-China Project, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA;5. School of Civil and Environmental Engineering, Georgia Institute of Technology, 790 Atlantic Drive, SEB Building, Atlanta, GA 30332, USA |
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| Abstract: | The variability of river inflows affects the energy production of hydropower generators and may result in reductions in revenues that can be financially disruptive for these producers. Recent climatic changes have highlighted the risks involved in hydroelectriciy production in Brazil. In this paper, we propose a different approach to formulating a collar derivative, namely an Inverted Collar, to mitigate hydrological risk considering the particularities of Brazil's energy regulatory environment. In addition, we propose a customized collar-by-difference as a variation of the collar model. The effect of these derivatives is analyzed considering electricity market price and power generation uncertainty for a typical hydro generator. The results suggest that these derivatives are effective tools to manage hydrological risk during period of great climatic volatility, such as the height of the drought period experienced by Brazil in 2016. The results also indicate that our models outperform traditional commercial hedging commonly practiced by hydropower producers in the country. |
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