Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness
Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-06-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/11/3351 |
| _version_ | 1797531037281026048 |
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| author | Takuji Matsumoto Yuji Yamada |
| author_facet | Takuji Matsumoto Yuji Yamada |
| author_sort | Takuji Matsumoto |
| collection | DOAJ |
| description | Previous studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis. |
| first_indexed | 2024-03-10T10:38:26Z |
| format | Article |
| id | doaj.art-ca3d91126f83493eb17cd25dcaa8c41b |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T10:38:26Z |
| publishDate | 2021-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-ca3d91126f83493eb17cd25dcaa8c41b2023-11-21T23:10:05ZengMDPI AGEnergies1996-10732021-06-011411335110.3390/en14113351Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring RobustnessTakuji Matsumoto0Yuji Yamada1Socio-Economic Research Center, Central Research Institute of Electric Power Industry, Tokyo 100-8126, JapanFaculty of Business Sciences, University of Tsukuba, Tokyo 112-0012, JapanPrevious studies have demonstrated that non-parametric hedging models using temperature derivatives are highly effective in hedging profit/loss fluctuation risks for electric utilities. Aiming for the practical applications of these methods, this study performs extensive empirical analyses and makes methodological customizations. First, we consider three types of electric utilities being exposed to risks of “demand”, “price”, and their “product (multiplication)”, and examine the design of an appropriate derivative for each utility. Our empirical results show that non-parametrically priced derivatives can maximize the hedge effect when a hedger bears a “price risk” with high nonlinearity to temperature. In contrast, standard derivatives are more useful for utilities with only “demand risk” in having a comparable hedge effect and in being liquidly traded. In addition, the squared prediction error derivative on temperature has a significant hedge effect on both price and product risks as well as a certain effect on demand risk, which illustrates its potential as a new standard derivative. Furthermore, spline basis selection, which may be overlooked by modeling practitioners, improves hedge effects significantly, especially when the model has strong nonlinearities. Surprisingly, the hedge effect of temperature derivatives in previous studies is improved by 13–53% by using an appropriate new basis.https://www.mdpi.com/1996-1073/14/11/3351electricity marketsnon-parametric regressionminimum variance hedgespline basis functionscyclic cubic splineweather derivatives |
| spellingShingle | Takuji Matsumoto Yuji Yamada Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness Energies electricity markets non-parametric regression minimum variance hedge spline basis functions cyclic cubic spline weather derivatives |
| title | Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness |
| title_full | Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness |
| title_fullStr | Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness |
| title_full_unstemmed | Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness |
| title_short | Customized yet Standardized Temperature Derivatives: A Non-Parametric Approach with Suitable Basis Selection for Ensuring Robustness |
| title_sort | customized yet standardized temperature derivatives a non parametric approach with suitable basis selection for ensuring robustness |
| topic | electricity markets non-parametric regression minimum variance hedge spline basis functions cyclic cubic spline weather derivatives |
| url | https://www.mdpi.com/1996-1073/14/11/3351 |
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