Realized Volatility and Price Spikes in Electricity Markets: The Importance of Observation Frequency

Ullrich, Carl J.

doi:10.2139/ssrn.1342586

Cited by 8 publications

(7 citation statements)

References 32 publications

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“…This model is indeed suitable to characterize the volatile nature of electricity prices. Reference [8] uses high frequency spot price data coming from several markets in order to estimate volatility and consequently the occurrence of price spikes.…”

Section: Literature Reviewmentioning

confidence: 99%

Price Spikes in the Italian Electricity Market: An Economic Analysis

Zedda¹,

Masala²

2019

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Since their liberalization, the electricity markets are characterized by a rather high volatility, which includes price spikes as one of the stylized and specific characteristics of this market. In this paper, we investigate the factors and mechanisms determining these spikes. Based on the Italian electricity market data, we perform a specific analysis of the auctions mechanisms and of the hourly bid and offer of electricity characteristics, for determining how and why do price spikes occur.

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Section: Literature Reviewmentioning

confidence: 99%

Price Spikes in the Italian Electricity Market: An Economic Analysis

Zedda¹,

Masala²

2019

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“…We control for return seasonality using two di¤erent …ltering approaches. The …rst approach used by Chan et al (2008), Ullrich (2012) and Ciarreta and Zarraga (2016), where the medians of the returns are subtracted from the returns for each month of the year, m, day of the week, d, and half-hour, hh, of the day to take into account the seasonal pattern e¤ect observed in the descriptive analysis of the prices, r t;(m;d;hh) . Therefore, the de-median returns are de…ned as r 1 t;j = r t;j r t;(m;d;hh) .…”

Section: Data and Descriptive Statisticsmentioning

confidence: 99%

Modelling Realized Volatility in Electricity Spot Prices: New Insights and Application to the Japanese Electricity Market

2017

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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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“…It is employed to identify significant price jumps and decompose total variation into its jump and nonjump components nonparametrically, as introduced by Barndorff- Nielsen and Shephard (2004) and modified by Andersen et al (2007) so as to prevent the process of jump detection from being downward biased. Applications to electricity markets can be found in Chan, Gray, and van Campen (2008), Ullrich (2012), Haugom, Westgaard, Solibakke, and Lien (2011), and Haugom and Ullrich (2012), among others. Additionally, other jump-robust test statistics have been developed using different types of estimators for continuous variation in price processes.…”

Section: Introductionmentioning

confidence: 99%

“…Ullrich (2012) andChan et al (2008) consider the standard deviation, whileCorsi et al (2008),Haugom and Ullrich (2012), andAndersen et al (2007) use both transformations.…”

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confidence: 99%

Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices

Ciarreta

Muniain

Zarraga

2017

Journal of Forecasting

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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.

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Realized Volatility and Price Spikes in Electricity Markets: The Importance of Observation Frequency

Cited by 8 publications

References 32 publications

Price Spikes in the Italian Electricity Market: An Economic Analysis

Price Spikes in the Italian Electricity Market: An Economic Analysis

Modelling Realized Volatility in Electricity Spot Prices: New Insights and Application to the Japanese Electricity Market

Modeling and forecasting realized volatility in German–Austrian continuous intraday electricity prices

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