Modelling Spikes in Electricity Prices
AbstractDuring periods of market stress, electricity prices can rise dramatically. Electricity retailers cannot pass these extreme prices on to customers because of retail price regulation. Improved prediction of these price spikes therefore is important for risk management. This paper builds a time-varying-probability Markov-switching model of Queensland electricity prices, aimed particularly at forecasting price spikes. Variables capturing demand and weather patterns are used to drive the transition probabilities. Unlike traditional Markov-switching models that assume normality of the prices in each state, the model presented here uses a generalised beta distribution to allow for the skewness in the distribution of electricity prices during high-price episodes. Copyright © 2007 The Economic Society of Australia.
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Bibliographic InfoArticle provided by The Economic Society of Australia in its journal Economic Record.
Volume (Year): 83 (2007)
Issue (Month): 263 (December)
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Other versions of this item:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models
- C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
- Q49 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Other
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- Volodymyr Korniichuk, 2012. "Forecasting extreme electricity spot prices," Cologne Graduate School Working Paper Series 03-14, Cologne Graduate School in Management, Economics and Social Sciences.
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- Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models for electricity spot prices," Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
- Higgs, Helen, 2009. "Modelling price and volatility inter-relationships in the Australian wholesale spot electricity markets," Energy Economics, Elsevier, vol. 31(5), pages 748-756, September.
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