Journal of the Korean Data and Information Science Society

The Korean Data and Information Science Society (한국데이터정보과학회)
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Pricing weather derivatives: An application to the electrical utility

  • Zou, Zhixia (Department of Business, Hunan Information Science Vocational College) ;
  • Lee, Kwang-Bong (Department of International Trade, Inje University)
  • Received : 2012.01.16
  • Accepted : 2012.02.17
  • Published : 2012.03.31
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Abstract

Weather derivatives designed to manage casual changes of weather, as opposed to catastrophic risks of weather, are relatively a new class of financial instruments. There are still many theoretical and practical challenges to the effective use of these instruments. The objective of this paper is to develop a pricing approach for valuing weather derivatives and presents a case study that is practical enough to be used by the risk managers of electrical utility firms. Utilizing daily average temperature data of Guangzhou, China from $1^{st}$ January 1978 to $31^{st}$ December 2010, this paper adopted a univariate time series model to describe weather behavior dynamics and calculates equilibrium prices for weather futures and options for an electrical utility firm in the region. The results imply that the risk premium is an important part of derivatives prices and the market price of risk affects option values much more than forward prices. It also demonstrates that weather innovation as well as weather risk management significantly affect the utility's financial outcomes.

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References

  1. Alaton, P., Djehiche, B. and Stillberger, D. (2002). On modelling and pricing weather derivatives. Applied Mathematical Finance, 9, 1-20. https://doi.org/10.1080/13504860210132897
  2. Benth, F. E. and Benth, J. S. (2005). The volatility of temperature and pricing of weather derivatives. Pure Mathematics, 12, 1-17.
  3. Calum G. T. (2005). The pricing of degree-day weather options. Agricultural Finance Review, 65, 59-85. https://doi.org/10.1108/00214660580001166
  4. Campbell, S. D. and Diebold, F. X. (2002). Weather forecasting for weather derivatives, PIER working paper. No. 02-046.
  5. Campbell, S. D. and Diebold, F. X. (2005). Weather forecasting for weather derivatives. Journal of American Statistical Association, 100, 6-16. https://doi.org/10.1198/016214504000001051
  6. Cao, M. andWei, J. Z. (1999). Pricing weather derivative: An equilibrium approach, Working Paper Series. Available at: http://ssrn.com/abstract=172414.
  7. Cao, M. andWei, J. Z. (2004). Weather derivatives valuation and market price of weather risk. The Journal of Futures Markets, 24, 1065-1089. https://doi.org/10.1002/fut.20122
  8. Chang, C. C., Lin, J. B. and Shen, W. M. (2009). Pricing weather derivatives using a predicting power time series process. Asia-Pacific Journal of Financial Studies, 38, 863-890.
  9. Duan, H. and Qian, H. (2009). Responses of electric power consumption to climate change in Guangzhou city. Journal of Applied Meteorological Science, 20, 80-87.
  10. Ellithorpe, D. and Putnam, S. (2000). Weather derivatives and their implications for power markets. Journal of Risk and Finance, 1, 19-28. https://doi.org/10.1108/eb043442
  11. Kang, M. S., Choi, H. S. and Park, B. C. (2011). Effects of environmental management on rm performance. Journal of the Korean Data & Information Science Society, 22, 523-536.
  12. Lee, H. (2009). Analysis of statistical models for ozone concentrations at the Paju city in Korea. Journal of the Korean Data & Information Science Society, 20, 1085-1092.
  13. Lee, H. (2010). Analysis of time series models for PM10 concentrations at the Suwon city in Korea. Journal of the Korean Data & Information Science Society, 21, 1117-1124.
  14. Lucas, R. E. Jr. (1978). Asset prices in an exchange economy. Econometrica, 46, 1429-1445. https://doi.org/10.2307/1913837
  15. Shim, J. and Hwang, C. (2011). Forecasting volatility via conditional autoregressive value at risk model based on support vector quantile regression. Journal of the Korean Data & Information Science Society, 22, 589-596.
  16. Zou, Z. X. (2011). Weather derivatives pricing: An application to Guangzhou electrical utility in China, Ph. D. Thesis Department of International Trade. Inje University.