A regime-switching stochastic volatility model for forecasting electricity prices

Oskar Knapik and Peter Exterkate ()

No 2017-02, Working Papers from University of Sydney, School of Economics

Abstract: In a recent review paper, Weron (2014) pinpoints several crucial challenges outstanding in the area of electricity price forecasting. This research attempts 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, iii) introducing an universal technique for model comparison. We propose new regime-switching stochastic volatility model with three regimes (negative jump, normal price, positive jump (spike)) where the transition matrix depends on explanatory variables. Bayesian inference is explored in order to obtain predictive densities. The main focus of the paper is on short-time density forecasting in Nord Pool intraday market. We show that the proposed model outperforms several benchmark models at this task.

Keywords: Electricity prices; density forecasting; Markov switching; stochastic volatility; fundamental price drivers; ordered probit model; Bayesian inference; seasonality; Nord Pool power market; electricity prices forecasting; probabilistic forecasting (search for similar items in EconPapers)
Date: 2017-02
New Economics Papers: this item is included in nep-ecm, nep-ene, nep-for and nep-mst
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Working Paper: A regime-switching stochastic volatility model for forecasting electricity prices (2017)
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