Abstract
This paper aims to study the forecasting capabilities of several models under the Markov regime-switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time-varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks-over-threshold (EVT-PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out-of-sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions, however we are unable to identify a single best model. The EVT-PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution (GPD) to model price extremities.
Published Version
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