An empirical comparison of alternate regime-switching models for electricity spot prices

Janczura, Joanna; Weron, Rafał

doi:10.1016/j.eneco.2010.05.008

Cited by 183 publications

(120 citation statements)

References 35 publications

(33 reference statements)

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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%

“…Our model is insofar different that we do not make any distributional assumptions for the spike process and require (for its simplest specification) only that F has support [a, ∞) for some threshold a corresponding to a pre-specified quantile of the time-series. The assumption that observations from the spike regime should not be smaller than a seems reasonable as the spike regime is used to model extreme (positive) prices and was already made earlier in the literature (see Weron, 2009 andWeron, 2010).…”

Section: Model Specificationmentioning

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%

“…After having set the framework, we choose parameter values, based on results from former publications using regime-switching models for electricity markets (see Janczura and Weron, 2010 and references therein) to conduct the simulation-studies. At the same time we restrict the spike process not to yield values that are smaller than the 90% quantile of the base process: To check if the simulation framework can be interpreted as realistic we give a graphical example.…”

Section: Simulation-studymentioning

confidence: 99%

“…An extension for VAR models followed in Haldrup et al (2010). An overview concerning the different extensions can be found in Janczura and Weron (2010).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Model Specificationmentioning

confidence: 99%

Section: Spike Probabilities and Transition Matrixmentioning

confidence: 99%

Section: Simulation-studymentioning

confidence: 99%

“…An extension for VAR models followed in Haldrup et al (2010). An overview concerning the different extensions can be found in Janczura and Weron (2010).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Fitting semiparametric Markov regime-switching models to electricity spot prices

Eichler

Türk

2013

Energy Economics

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MCMC calibration of spot‐prices models in electricity markets

Guerini¹,

Marziali

Nicolao

2019

Appl Stoch Models Bus & Ind

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The calibration of some stochastic differential equation used to model spot prices in electricity markets is investigated. As an alternative to relying on standard likelihood maximization, the adoption of a fully Bayesian paradigm is explored, that relies on Markov chain Monte Carlo (MCMC) stochastic simulation and provides the posterior distributions of the model parameters. The proposed method is applied to one‐ and two‐factor stochastic models, using both simulated and real data. The results demonstrate good agreement between the maximum likelihood and MCMC point estimates. The latter approach, however, provides a more complete characterization of the model uncertainty, an information that can be exploited to obtain a more realistic assessment of the forecasting error. In order to further validate the MCMC approach, the posterior distribution of the Italian electricity price volatility is explored for different maturities and compared with the corresponding maximum likelihood estimates.

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Analyzing and forecasting electricity price using regime‐switching models: The case of New Zealand market

Kapoor

Wichitaksorn

Zhang

2023

Journal of Forecasting

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This paper aims to study the forecasting capabilities of several models under the Markov regime‐switching (MRS) and the extreme value theory (EVT) frameworks applied to daily electricity prices in the New Zealand electricity market. The MRS models in this study include up to five regimes, with time‐varying transition probabilities and incorporation of external market variables. We apply Hamilton's filter with maximum likelihood estimation for parameter estimation. The EVT peaks‐over‐threshold (EVT‐PoT) framework is also considered, and its relationship to the MRS class of models is discussed. We generate out‐of‐sample forecasts under various market scenarios. The MRS models are able to replicate real price densities under stable market conditions. The EVT‐PoT model performs well despite its lack of complexity compared to the MRS framework. We attribute this to the usage of the generalized Pareto distribution to model price extremities.

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An empirical comparison of alternate regime-switching models for electricity spot prices

Cited by 183 publications

References 35 publications

Fitting semiparametric Markov regime-switching models to electricity spot prices

Fitting semiparametric Markov regime-switching models to electricity spot prices

MCMC calibration of spot‐prices models in electricity markets

Analyzing and forecasting electricity price using regime‐switching models: The case of New Zealand market

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