Bayesian estimation of electricity price risk with a multi-factor mixture of densities

The risks in daily electricity prices are becoming substantial and it is clear that improvements in price density forecasting can translate into improved risk management. However, the specification of the most appropriate price density function is challenging as the best functional forms differ by time of day evolve over time, dynamically respond to fluctuating exogenous factors such as wind speed and solar irradiance. This research develops and tests a new flexible, functional form based upon the Gamma Mixture of Uniform (GMU) densities which effectively avoids the choice of a particular density function and has conditional moments specified as a function of the dynamic exogenous drivers. Empirical testing shows that it outperforms the multi-factor skewed student-t family of densities, previously advocated in this context. Additionally, using Bayesian estimation the new methodology provides a complete description of the uncertainty in the estimation of the coefficients for those exogenous factors. Empirical testing on day-ahead hourly electricity prices in the German market from 2012 to 2016, where renewable energy sources, such as wind and solar, play a critical role in the formation of electricity price risk, validates the extra accuracy of this formulation.