Simultaneous hedging strategy for price and volume risks in electricity businesses using energy and weather derivatives

Abstract

In general, electric utilities face intrinsic risks as their revenues depend on high volatility factors including price and volume of sales/procurements. Aiming for an effective strategy to control those risks, we construct a hedging portfolio based on energy and weather derivatives, which can minimize the revenue fluctuations. To this end, we provide unique methods by applying nonparametric regression techniques to synthesize the payoff functions of derivatives that change with time, based on tensor product spline functions. The proposed methodology enables us to incorporate two dimensional smoothing conditions of the underlying asset price and expiration date with a yearly cyclical trend. Moreover, we show that the applied method of Analysis of Variance (ANOVA) decomposition can separate deterministic time trends from the original multivariate payoff functions, and hence, a simultaneous estimation of multiple derivatives payoff functions is achieved. By assuming that revenues have yearly cyclical trends even when viewed at the rate of annual change, we also introduce a spline function with cross variables to consider such a mixed effect. In addition, we propose new standardized derivatives with the square of the temperature prediction error as the underlying asset. Empirical analysis using data from both the Japan Electric Power Exchange (JEPX) and PJM in the U.S. demonstrates the significant hedging effect and supports the versatility of the proposed modeling approach.