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Independent Spike Models: Estimation and Validation

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Abstract

The authors apply a class of Markov switching models (independent spike models) to six European electricity markets and two European gas markets. This paper extends the current framework by introducing Gamma distributed spikes, which improves the fit for most energy markets. The models are quite complex. The robustness of the estimates is therefore evaluated using three different estimation strategies: direct maximization of the likelihood function, the Expectation-Maximization algorithm, and Markov Chain Monte Carlo (MCMC). The seasonal variation is corrected for by using the month-ahead forward price as a predictor. The models provide good empirical results for most markets.

Suggested Citation

  • Erik Lindström & Fredric Regland, 2012. "Independent Spike Models: Estimation and Validation," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 62(2), pages 180-196, May.
    • Handle: RePEc:fau:fauart:v:62:y:2012:i:2:p:180-196
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    Cited by:

    1. Lindström, Erik & Norén, Vicke & Madsen, Henrik, 2015. "Consumption management in the Nord Pool region: A stability analysis," Applied Energy, Elsevier, vol. 146(C), pages 239-246.
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    3. Lindström, Erik & Regland, Fredrik, 2012. "Modeling extreme dependence between European electricity markets," Energy Economics, Elsevier, vol. 34(4), pages 899-904.

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    More about this item

    Keywords

    regime switching models; electricity spot prices; independent spike models; gamma distribution;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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