Abstract
With the recent introduction of wind power generation of various scales due to its promise as a green energy resource, effectively managing the risk of fluctuations in wind power generation revenues has become an important issue. Against this background, this study introduces several weather derivatives based on wind speed and temperature as underlying assets and examines their effectiveness. In particular, we propose new standardized derivatives with higher-order monomial payoff functions, such as “wind speed cubic derivatives” and “wind speed and temperature cross derivatives.” In contrast to the existing nonparametric derivatives, the minimum variance hedging problem to find the optimal contract amount of these standardized derivatives is reduced to estimating a linear regression. We also develop a market trading model to put the proposed standardized derivatives into practical use and clarify the real-world implications of standardizing weather derivatives. Furthermore, to make trading more efficient, we propose a “product selection” strategy utilizing the “variable selection” approach of LASSO regression. Empirical analysis confirms hedging effectiveness comparable to existing nonparametric derivatives and reveals the effectiveness of the proposed derivatives standardization scheme as well as their trading strategies.
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Acknowledgements
This work was funded by a Grant-in-Aid for Scientific Research (A) 20H00285, Grant-in-Aid for Challenging Research (Exploratory) 19K22024, and Grant-in-Aid for Young Scientists 21K14374 from the Japan Society for the Promotion of Science (JSPS).
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Matsumoto, T., Yamada, Y. (2023). Multivariate Weather Derivatives for Wind Power Risk Management: Standardization Scheme and Trading Strategy. In: Caetano, N.S., Felgueiras, M.C. (eds) The 9th International Conference on Energy and Environment Research. ICEER 2022. Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-031-43559-1_26
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