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2022 | OriginalPaper | Buchkapitel

Modelling and Forecasting the Volatility of the Nordic Power Market: An Application of the GARCH-Jump Process

verfasst von : Anupam Dutta

Erschienen in: Revisiting Electricity Market Reforms

Verlag: Springer Nature Singapore

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Abstract

Although extreme jumps in electricity prices are a common phenomenon, investigating the jump behaviour in the power market does not receive significant attention in earlier studies. The present study aims to conceal this void in the existing literature. To do so, we employ the autoregressive conditional jump intensity (ARJI) model, combined with the generalised autoregressive conditional heteroskedasticity (GRACH) method, to describe the volatility process and the jump behaviour in Nordic electricity prices. The empirical findings reveal that the Nordic power market is highly volatile, and time-varying jumps exist in the electricity prices. In addition, the GARCH-jump models produce more accurate out-of-sample volatility forecasts than the GARCH and EGARCH models. In summary, the results demonstrate that energy economists, energy policymakers, and market analysts should consider the existence of time-varying jumps in the Nordic power market because the GARCH-jump model provides the best forecasts for electricity prices.

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Fußnoten
1
The information is sourced from Fortum Energy Review, November (2016).
 
2
The information is sourced from www.​nordicenergyregu​lators.​org.
 
3
The term ‘deregulation’ means that the state is no longer running the power market; instead, free competition is introduced. Deregulation is undertaken to create a more efficient market, with exchange of power between countries and increased security of supply. Available power capacity can be used more efficiently in a large region than in a small one, and integrated markets enhance productivity and improve efficiency (for more details, see www.​nordpoolgroup.​com).
 
4
The data are retrieved from https://​www.​nordpoolgroup.​com/​.
 
5
Selection of the mean and variance equations is based on the Akaike Information criterion (AIC) and Bayesian Information criterion (BIC). We first estimate the AR(1)-GARCH(1,1) model. In addition, several alternative models are also considered. These include AR(2)-GARCH(1,1), AR(3)-GARCH(1,1), AR(2)-GARCH(2,1), AR(2)-GARCH(2,2), amongst others. But based on AIC and BIC statistics, we finally choose the AR(2)-GARCH(1,1) model as it produces the lowest values for AIC and BIC. Once the appropriate lags have been identified, we test for the autocorrelation amongst the residuals to verify whether the selected model is correctly fitted.
 
6
We consider the GARCH (1,1) and EGARCH (1,1) approaches in our analysis as the benchmark models. These models are defined as follows:
GARCH (1,1): \({h}_{t}=\omega +\alpha {\varepsilon }_{t-1}^{2}+\beta {h}_{t-1}\), where \(\omega >0\), \(\alpha \ge 0\), \(\beta \ge 0\) and \(\upgamma \ge 0\) to guarantee the positivity of \({h}_{t}\).
EGARCH (1,1): \(\mathrm{ln}\left({h}_{t}\right)=c+\frac{a\left|{\varepsilon }_{t-1}\right|+v{\varepsilon }_{t-1}}{\sqrt{{h}_{t-1}}}+b\mathrm{ln}{(h}_{t-1}).\)
 
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Metadaten
Titel
Modelling and Forecasting the Volatility of the Nordic Power Market: An Application of the GARCH-Jump Process
verfasst von
Anupam Dutta
Copyright-Jahr
2022
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-19-4266-2_6