Modeling and forecasting multivariate electricity price spikes

Hans Manner*, Dennis Tuerk, Michael Eichler

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

18 Citations (Web of Science)

Abstract

We consider the problem of forecasting the occurrence of extreme prices in the australian electricity markets from high frequency spot prices. In particular, we are interested in the simultaneous occurrence of such so-called spikes in two or more markets. Our approach is based on a novel dynamic model for multivariate binary outcomes, which allows the latent variables driving these observed outcomes to follow a vector autoregressive process. Furthermore the model is constructed using a copula representation for the joint distribution of the resulting innovations. This has several advantages over the standard multivariate probit model. First, it allows for nonlinear dependence between the error terms. Second, the distribution of the latent errors can be chosen freely. Third, the computational burden can be greatly reduced making estimation feasible in higher dimensions and for large samples. The model is applied to spikes in half-hourly electricity prices in four interconnected australian markets. The multivariate model provides a superior fit compared to single-equation models and generates better forecasts for spike probabilities. Furthermore, evidence of spillover dynamics between the markets is revealed.
Original languageEnglish
Pages (from-to)255-265
Number of pages11
JournalEnergy Economics
Volume60
DOIs
Publication statusPublished - Nov 2016

Keywords

  • Electricity price spikes
  • Multivariate binary choice models
  • Copulas
  • Vector autoregression

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