CMStatistics 2017: Start Registration
View Submission - CFE
Title: A regime-switching stochastic volatility model for forecasting electricity prices Authors:  Peter Exterkate - University of Sydney (Australia) [presenting]
Oskar Knapik - Aarhus University (Denmark)
Abstract: In a recent review paper, several crucial challenges outstanding in the area of electricity price forecasting are pinpointed. The aim is to address all of them by (i) showing the importance of considering fundamental price drivers in modeling, (ii) developing new techniques for probabilistic (i.e. interval or density) forecasting of electricity prices, and (iii) introducing an universal technique for model comparison. We propose a new regime-switching stochastic volatility model with three regimes (negative jump or ``drop'', normal price or ``base'', positive jump or ``spike'') where the transition matrix depends on explanatory variables. Bayesian inference is employed in order to obtain predictive densities. The main focus is on short-term density forecasting in the Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task.