Cointegration between gas and power spot prices

Jong, Cyriel de; Schneider, Stefan

doi:10.21314/jem.2009.023

Cited by 31 publications

(25 citation statements)

References 20 publications

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“…As a side product, the procedure allows to account for negative outliers without making it necessary to model them specifically. In this context extensions to account for a third regime with negative spikes as proposed in De Jong and Schneider (2009) and Janczura and Weron (2010) would be straight forward. However the data at hand exhibited very scarce occurrence of negative outliers, making it advantageous to robustify the estimation against these occurrences without modeling them explicitly.…”

Section: Resultsmentioning

confidence: 99%

“…Extending our approach to allow the modeling of two different spike regimes would be straight forward and might be interesting for alternate datasets in which negative prices are more frequent. Furthermore the use of a multivariate framework in order to yield further results in the line with the publications of De Jong and Schneider (2009) and Lindstroem and Regland (2012) would be of great interest. The last point is even more interesting when considering the computational ease that the proposed approach offers compared to former estimation procedures used in the literature.…”

Section: Resultsmentioning

confidence: 99%

“…In case of frequent negative spikes an additional second spike regime can be introduced to include these observations in the model (see e.g. Kosater and Mosler, 2006, De Jong and Schneider, 2009and Janczura and Weron, 2010. The extension of the estimation method to this case is straightforward.…”

Section: Spike Probabilities and Transition Matrixmentioning

confidence: 99%

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Fitting semiparametric Markov regime-switching models to electricity spot prices

Eichler

Türk

2013

Energy Economics

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Recently regime-switching models have become the standard tool for modeling electricity prices. These models capture the main properties of electricity spot prices well but estimation of the model parameters requires computer intensive methods. Moreover, the distribution of the price spikes must be assumed given although the high volatility of the spikes makes it difficult to check this assumption. Consequently, there are a number of competing proposals. Alternatively we propose the use of a semiparametric Markov regime-switching model that does not specify the distribution under the spike regime. To estimate the model we use robust estimation techniques as an alternative to commonly applied estimation approaches. The model in combination with the estimation framework is easier to estimate, needs less computation time and distributional assumptions. To show its advantages we compare the proposed model with a well established Markov-switching model in a simulation-study. Further we apply the model to Australian logprices. The results are in accordance with the results from the simulation-study, indicating that the proposed model might be advantageous whenever the distribution of the spike process is not sufficiently known. The results are thus encouraging and suggest the use of our approach when modeling electricity prices and pricing derivatives.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Fitting semiparametric Markov regime-switching models to electricity spot prices

Eichler

Türk

2013

Energy Economics

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“…[2], multiple energy spot prices such as gas and power between different markets are considered. A cointegrating multi-market model framework is developed by connecting different single-market spot-price models and it is shown that gas prices are strongly co-integrated with power prices for a specific market.…”

Section: Introductionmentioning

confidence: 99%

“…Present work is based on [2], where it is shown that ignoring co-integration and using simple correlation based price models heavily overestimates the spread variation over long time horizons. This can lead to drastically overestimate Value-at-Risk and excessive capital requirements.…”

Section: Introductionmentioning

confidence: 99%

Co-integration for Soft Commodities with Non Constant Volatility

Chanol¹,

Collet²,

Kostyuchyk³

et al. 2015

IJTEF

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Abstract-In this paper, a pricing model is proposed for cointegrated commodities extending Larsson model. The futures formulae have been derived considering a linear combination of a Brownian motion and an Ornstein-Uhlenbeck process describing the co-integration relationship of the different futures prices commodities. Tests have been performed with a non-constant volatility in order to fit better the real behavior of the volatility. The model has been applied to energy commodities (gas, CO2, energy) and soft commodities (corn, wheat). Results show that first, the model can be used with different kind of commodities at the cost of a proper parameters calibration and in second, using a non-constant volatility leads to more accurate short term prices, which provides better evaluation of Value-at-Risk and more generally improves the risk management.

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Multivariate Volatility Modeling of Electricity Futures

Bauwens

Hafner

Pierret

2012

J of Applied Econometrics

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The deregulation of European electricity markets has led to an increasing need in understanding the volatility and correlation structure of electricity prices. We model a multivariate futures series of the European Energy Exchange (EEX) index, using an asymmetric GARCH model for volatilities and augmented dynamic conditional correlation (DCC) models for correlations. In particular, we allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate non-parametrically. We also introduce exogenous variables in our new multiplicative DCC model to account for congestion in short-term conditional volatilities. We find different correlation dynamics for long and short-term contracts and the new model achieves higher forecasting performance compared to a standard DCC model. AbstractThe deregulation of European electricity markets has led to an increasing need in understanding the volatility and correlation structure of electricity prices. We model a multivariate futures series of the European Energy Exchange (EEX) index, using an asymmetric GARCH model for volatilities and augmented dynamic conditional correlation (DCC) models for correlations. In particular, we allow for smooth changes in the unconditional volatilities and correlations through a multiplicative component that we estimate non-parametrically. We also introduce exogenous variables in our new multiplicative DCC model to account for congestion in short-term conditional volatilities. We find different correlation dynamics for long and short-term contracts and the new model achieves higher forecasting performance compared to a standard DCC model.

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Cointegration between gas and power spot prices

Cited by 31 publications

References 20 publications

Fitting semiparametric Markov regime-switching models to electricity spot prices

Fitting semiparametric Markov regime-switching models to electricity spot prices

Co-integration for Soft Commodities with Non Constant Volatility

Multivariate Volatility Modeling of Electricity Futures

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