Modelling Spikes in Electricity Prices*

Becker, Ralf; Hurn, Stan; Pavlov, Vlad

doi:10.1111/j.1475-4932.2007.00427.x

Cited by 50 publications

(46 citation statements)

References 6 publications

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“…While it is clear that price spikes should be captured by an adequate stochastic model, like mean reverting jump-diffusion (Bierbrauer et al, 2007;Borovkova and Permana, 2006;Cartea and Figueroa, 2005;Clewlow and Strickland, 2000;Geman and Roncoroni, 2006;Jabłońska et al, 2011;Nomikos and Soldatos, 2010;Seifert and Uhrig-Homburg, 2007;Weron, 2008) or a regimeswitching model (Becker et al, 2007;De Jong, 2006;Higgs and Worthington, 2008;Hirsch, 2009;Huisman and Mahieu, 2003;Weron, 2010, 2012;Keles et al, 2012;Mari, 2008;Mount et al, 2006;Weron, 2009), the literature does not agree on whether these observations have to be included or excluded in the estimation of the seasonal pattern.…”

Section: Introductionmentioning

confidence: 99%

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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An important issue in fitting stochastic models to electricity spot prices is the estimation of a component to deal with trends and seasonality in the data. Unfortunately, estimation routines for the long-term and short-term seasonal pattern are usually quite sensitive to extreme observations, known as electricity price spikes. Improved robustness of the model can be achieved by (a) filtering the data with some reasonable procedure for outlier detection, and then (b) using estimation and testing procedures on the filtered data. In this paper we examine the effects of different treatment of extreme observations on model estimation and on determining the number of spikes (outliers). In particular we compare results for the estimation of the seasonal and stochastic components of electricity spot prices using either the original or filtered data. We find significant evidence for a superior estimation of both the seasonal short-term and long-term components when the data have been treated carefully for outliers. Overall, our findings point out the substantial impact the treatment of extreme observations may have on these issues and, therefore, also on the pricing of electricity derivatives like futures and option contracts. An added value of our study is the ranking of different filtering techniques used in the energy economics literature, suggesting which methods could be and which should not be used for spike identification.

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Section: Introductionmentioning

confidence: 99%

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

et al. 2012

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“…This group includes regression, autoregressive (AR) (Lucia and Schwartz, 2002;Pilipovic, 1998), moving average (MA), autoregressive moving average (ARMA) (Bowden and Payne, 2008), autoregressive integrated moving average (ARIMA) (Erdogdu, 2007(Erdogdu, , 2010, GARCH models and their variants, jump diffusion models (Clewlow and Strickland, 2000;Deng, 2000;Knittel and Roberts, 2005;Seifert and Uhrig-Homburg, 2007) and the Markov regimeswitching models (Becker et al, 2007;Bierbrauer et al, 2007;Huisman and Mahieu, 2003;Kosater and Mosler, 2006). As our work clearly contributes to this line of research, more examples from it are presented below.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

Erdoğdu

2016

Energy Economics

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This paper uses high frequency spot price data from fourteen wholesale electricity markets in Europe to analyze asymmetric volatility in European day-ahead power markets with Exponential GARCH (E-GARCH) and TARCH models. Our data set ranges from 1992 to 2015 and consists of approximately 926 thousand observations. As such, this paper constitutes the most extensive and comprehensive work conducted so far on European power markets, to the best of our knowledge. Unlike most of the literature that treats price as a continuous Volatility has decreased in many European countries in post-2008 period. Besides, we find magnitude effect is usually larger than the leverage effect, meaning that the absolute value of price change is relatively more important than the sign of the change (whether it is an increase or a decrease) to explain volatility in European day-ahead power markets.Moreover, the results imply there isn't a uniform inverse leverage effect in electricity prices; that is, price increases are more destabilizing in some European markets (e.g.

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“…Markov switching models are the most prominent among those in which the switching mechanism between the states cannot be determined by an observable variable. For the treatment of spikes, they suggest different states in which at least one is consistent with its appearance [26]. With regard to continuous-time diffusion processes, spikes are essentially captured by the combination of a Poisson jump component and an intensity parameter.…”

Section: Previous Workmentioning

confidence: 99%

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

2016

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Abstract:One of the most relevant challenges that have arisen in electricity markets during the last few years is the emergence of extremely low prices. Trying to predict these events is crucial for market agents in a competitive environment. This paper proposes a novel methodology to simultaneously accomplish punctual and probabilistic hourly predictions about the appearance of extremely low electricity prices in a medium-term scope. The proposed approach for making real ex ante forecasts consists of a nested compounding of different forecasting techniques, which incorporate Monte Carlo simulation, combined with spatial interpolation techniques. The procedure is based on the statistical identification of the process key drivers. Logistic regression for rare events, decision trees, multilayer perceptrons and a hybrid approach, which combines a market equilibrium model with logistic regression, are used. Moreover, this paper assesses whether periodic models in which parameters switch according to the day of the week can be even more accurate. The proposed techniques are compared to a Markov regime switching model and several naive methods. The proposed methodology empirically demonstrates its effectiveness by achieving promising results on a real case study based on the Spanish electricity market. This approach can provide valuable information for market agents when they face decision making and risk-management processes. Our findings support the additional benefit of using a hybrid approach for deriving more accurate predictions.

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Modelling Spikes in Electricity Prices*

Cited by 50 publications

References 6 publications

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Identifying Spikes and Seasonal Components in Electricity Spot Price Data: A Guide to Robust Modeling

Asymmetric volatility in European day-ahead power markets: A comparative microeconomic analysis

Medium-Term Probabilistic Forecasting of Extremely Low Prices in Electricity Markets: Application to the Spanish Case

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