We propose a methodology to price an insurance contract designed to hedge the volumetric risk associated with weather conditions. Our methodology is based on conditional quantile regressions and adapts Value at Risk (VaR) and Expected Shortfall statistics from the literature on financial econometrics. In our empirical application, we use actual daily meteorological and radiation data for 40 European cities in 13 countries, from April 1, 2011, to December 31, 2021, and calculate the value of the annual insurance premium under reasonable assumptions on the technology of the solar panels and expected energy demand. We consider variables such as temperature, wind speed, and precipitation in our calculations. Our results for the different cities in our sample show that due to the nonlinear impact of weather (mainly temperature and precipitation) on the expected generation losses, it is appropriate to use quantile regressions to calculate the VaR of radiation, conditional on weather factors. Our insurance premium calculations also present a high degree of variation across European, which indicates that risk-diversification opportunities of catastrophic risk due to weather conditions faced by insurance companies exist. This variation is explained by different weather conditions, model adjustment, and the average price of electricity.
