This paper uses high‐frequency continuous intraday electricity price data from the EPEX market to estimate and forecast realized volatility. Three different jump tests are used to break down the variation into jump and continuous components using quadratic variation theory. Several heterogeneous autoregressive models are then estimated for the logarithmic and standard deviation transformations. Generalized autoregressive conditional heteroskedasticity (GARCH) structures are included in the error terms of the models when evidence of conditional heteroskedasticity is found. Model selection is based on various out‐of‐sample criteria. Results show that decomposition of realized volatility is important for forecasting and that the decision whether to include GARCH‐type innovations might depend on the transformation selected. Finally, results are sensitive to the jump test used in the case of the standard deviation transformation.
The paper analyzes volatility of the electricity prices in the Japanese day-ahead market using realized volatility. We use several jump tests to decompose total realized variation into jump and continuous components. Then, we estimate several HAR models that show the time-dependence structure of the volatility. Our results show that even though that market is narrow, it is relevant to identify jumps in volatility. Besides, modeling residuals improve estimation results. The timedependent structure of the prices is present in volatility as well.
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