The Impact of Forecasting Jumps on Forecasting Electricity Prices

The paper is devoted to forecasting hourly day-ahead electricity prices from the perspective of the existence of jumps. We compare the results of different jump detection techniques and identify common features of electricity price jumps. We apply the jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables. In order to improve the accuracy of electricity price forecasts, we take into account the time-varying intensity of price jump occurrences. We forecast moments of jump occurrences depending on several factors, including seasonality and weather conditions, by means of the generalised ordered logit model. The study is conducted on the basis of data from the Nord Pool power market. The empirical results indicate that the model with the time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours, i.e., including the probabilities of downward, no or upward jump occurrences into the model improves the forecasts of electricity prices.

Automated summary

The paper focuses on forecasting hourly day-ahead electricity prices by considering the existence of jumps. It compares different jump detection techniques and identifies common features of electricity price jumps. By applying a jump-diffusion model with a double exponential distribution of jump sizes and explanatory variables, the study takes into account the time-varying intensity of price jump occurrences to improve the accuracy of price forecasts. Additionally, it forecasts moments of jump occurrences based on factors such as seasonality and weather conditions using the generalised ordered logit model. Empirical results suggest that incorporating a model with time-varying intensity of jumps and a mechanism of jump prediction is useful in forecasting electricity prices for peak hours.