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Modeling and forecasting multivariate electricity price spikes

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  • Manner, Hans
  • Türk, Dennis
  • Eichler, Michael

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.

Suggested Citation

  • Manner, Hans & Türk, Dennis & Eichler, Michael, 2016. "Modeling and forecasting multivariate electricity price spikes," Energy Economics, Elsevier, vol. 60(C), pages 255-265.
  • Handle: RePEc:eee:eneeco:v:60:y:2016:i:c:p:255-265
    DOI: 10.1016/j.eneco.2016.10.006
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    References listed on IDEAS

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    1. repec:eee:eneeco:v:74:y:2018:i:c:p:886-903 is not listed on IDEAS
    2. repec:eee:appene:v:212:y:2018:i:c:p:244-264 is not listed on IDEAS

    More about this item

    Keywords

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

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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