Heavy tails and electricity prices: Do time series models with non-Gaussian noise forecast better than their Gaussian counterparts?
AbstractThis paper is a continuation of our earlier studies on short-term price forecasting of California electricity prices with time series models. Here we focus on whether models with heavy-tailed innovations perform better in terms of forecasting accuracy than their Gaussian counterparts. Consequently, we limit the range of analyzed models to autoregressive time series approaches that have been found to perform well for pre-crash California power market data. We expand them by allowing for heavy-tailed innovations in the form of α-stable or generalized hyperbolic noise.
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Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 2292.
Date of creation: Mar 2007
Date of revision: Oct 2007
Electricity; price forecasting; heavy tails; time series; α-stable distribution; generalized hyperbolic distribution;
Find related papers by JEL classification:
- C46 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Specific Distributions
- 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
- Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-03-24 (All new papers)
- NEP-ECM-2007-03-24 (Econometrics)
- NEP-ENE-2007-03-24 (Energy Economics)
- NEP-ETS-2007-03-24 (Econometric Time Series)
- NEP-FOR-2007-03-24 (Forecasting)
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