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Introduction
Weather derivatives are generally regarded as the financial instruments for stabilizing incomes and minimizing losses in the weather related business activities. Considering the opportunity to use weather derivatives for insuring against global warming and its consequences, it should be stated that these attempts will be closely associated with the matters of prediction models.
Derivatives
First, it should be emphasized that the most effective way of insuring against weather derivatives is to use hybrid tools, as the threat of global warming has increased the liquidity of these derivatives. However, considering the nature of global warming, and rates of temperature volatility, futures may be regarded as one of the most effective insuring tool. (Campbell and Diebold, 2005) Therefore, it is generally assumed that the derivative will be of global nature, as the attempts to mitigate the risks associated with global warming will be of global nature either. The greenhouse effect minimization will be associated with the implementation of low-carbon emission technologies, and changing the everyday behavioral pattern. This will involve the opportunity of changing the trading schemes, as well as minimization of the losses associated with non-forecasted temperature volatility. As it is stated by Hung-Hsi and Yung-Ming (2008, p. 799):
We can utilize the output of Numerical weather prediction models based on physical equation describing relationships in the weather system. Their predictive power tends to be less or similar to purely statistical models beyond time horizons of 10–15 days. Ensemble forecasts are especially appropriate for weather derivative pricing within the contract period of a monthly temperature derivative. However, individuals members of the ensemble need to be ‘dressed’ before a reasonable probabilistic forecast can be obtained.
In the light of this fact, it should be emphasized that the actual importance of the forecasting activity will be crucial for the overall stability of the derivative tool. Prediction of the earnings’ volatility is generally based on the financial background of the market, while weather forecasts do not offer reliable data for the overall analysis of the economic situation.
As for the matters of the recommendations and real-life instances of the weather derivatives, and hedging against global warming caused losses, UBS recommends to consider the rates of Carbon Emission Credit, and the general assessments associated with the opportunity to evaluate the consequences of the economic background changes. This will be helpful for the overall assessment of the possible weather changes, and temperature volatility will be regarded as the independent variable that is needed for assessing and analyzing weather oriented business activities. Therefore, as it is emphasized by Hardle and Cabrera (2009, p. 17):
The index is built by aggregating the heating degree day (HDD) and cooling degree day (CDD) futures contracts for 15 U.S. cities. These contracts have traded for years on the CME. Each contract is a measure of the relative warmth or coolness of a different city; theoretically, they capture a measure of how much people need to air condition or heat their houses on a given day.
Hence, the futures that are needed for proper risk mitigation will be based on the previous experience of cooperation with the weather oriented business companies.
Conclusion
Weather derivatives may be used for hedging against the consequences of global warming by the means of futures. However, some additional parameters should be regarded. These are mainly linked with the previous business experience, as well as prediction models used.
Reference List
Campbell, S. and Diebold, F. 2005. Weather forecasting for weather derivatives, American Stat. Assoc. 100(469): 6–16.
Hardle, W. K., Cabrera, B. L. 2009. Implied Market Price of Weather Risk. Humboldt-Universität zu Berlin, Germany.
Hung-Hsi, H., Yung-Ming, S. 2008. HDD and CDD Option Pricing with Market Price of Weather Risk for Taiwan, The Journal of Future Markets 28(8): 790–814.
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