Pricing electricity day-ahead cap futures with multifactor skew-t densities

Short-term risk management is becoming increasingly significant in power trading as the intermittent renewable generators introduce more weather risk into the price formation dynamics. There is a vacuum in hedging instruments at the day-ahead stage to protect retailers in particular from such volatility and price spikes. Motivated by this requirement, this paper analyses a flexible hedging product, day-ahead cap futures. For pricing this product, we parametrically predict the probability distribution of day-ahead prices using the multifactor Generalized Additive Model for Location, Scale and Shape (GAMLSS) based upon the skew-t distribution with weather forecasts and calendar information as explanatory variables. In particular, we reveal that this higher-order moment model is superior to several lower-order models such as the normal distribution in all the following three aspects: fairness as pricing method, underwriting risk of the risk-taker and the variance reduction effect of the risk hedger.

Automated summary

Short-term risk management is crucial in power trading with the introduction of more weather risk by intermittent renewable generators. This paper analyzes a flexible hedging product, day-ahead cap futures, and parametrically predicts the probability distribution of day-ahead prices using the multifactor Generalized Additive Model. The higher-order moment model is shown to be superior in terms of fairness, underwriting risk, and variance reduction compared to lower-order models like the normal distribution.