Abstract
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.
Highlights
Electric utilities are generally exposed to the risk of daily fluctuations in price and demand, and constructing an efficient hedging methodology is an extremely important management issue
In this study, paying attention to the fact that different types of electric utilities are exposed to risks of demand, price, and both, we verified the hedge effects for each of the three business risk models (“demand model,” “price model,” and “product model”) using a previously proposed temperature derivative portfolio estimated using non-parametric hedging models
The nonlinearity of the temperature derivative payoffs by the business risk model is strong in the product model and the price model, and relatively weak in the demand model
Summary
Electric utilities are generally exposed to the risk of daily fluctuations in price and demand, and constructing an efficient hedging methodology is an extremely important management issue. The most recent study [14] demonstrated that portfolios of weather derivatives may be constructed by applying generalized additive models (GAMs [15]), which provide a significantly high hedge effect for the fluctuation risks in electricity sales profit/loss defined by the “product of price and demand”. If the hedged target contains either only demand or price risk, the nonlinear effect of temperature or other weather indices is supposedly weakened; rather than designing a completely made-to-order derivative, as in previous studies [14], using “customized yet standardized” derivatives are expected to have the advantages of having sufficient hedging effects (or maybe comparable to the “made-to-order” type), as well as high versatility in that they can be traded among.
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