Analysis of Forecasting Models in an Electricity Market under Volatility

Abstract

Short-term electricity price forecasting has received considerable attention in recent years. Despite this increased interest, the literature lacks a concrete consensus on the most suitable forecasting approach. We conduct an extensive empirical analysis to evaluate the short-term price forecasting dynamics of different regions in the Swedish electricity market (SEM). We utilized several forecasting approaches ranging from standard conditional volatility models to wavelet-based forecasting. In addition, we performed out-of-sample forecasting and back-testing, and we evaluated the performance of these models. Our empirical analysis indicates that an ARMA-GARCH framework with the student’s t-distribution significantly outperforms other frameworks. We only performed wavelet-based forecasting based on the MAPE. The results of the robust forecasting methods are capable of displaying the importance of proper forecasting process design, policy implications for market efficiency, and predictability in the SEM.