Weron, Rafal (2009): Forecasting wholesale electricity prices: A review of time series models. Published in: Financial Markets: Principles of Modelling, Forecasting and Decision-Making , Vol. FindEc, (2009): pp. 71-82.
Preview |
PDF
MPRA_paper_21299.pdf Download (254kB) | Preview |
Abstract
In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. We calibrate autoregression (AR) models, including specifications with a fundamental (exogenous) variable - system load, to California Power Exchange (CalPX) system spot prices. Then we evaluate their point and interval forecasting performance in relatively calm and extremely volatile periods preceding the market crash in winter 2000/2001. In particular, we test which innovations distributions/processes - Gaussian, GARCH, heavy-tailed (NIG, alpha-stable) or non-parametric - lead to best predictions.
Item Type: | MPRA Paper |
---|---|
Original Title: | Forecasting wholesale electricity prices: A review of time series models |
Language: | English |
Keywords: | Electricity price forecasting; heavy tailed distribution; autoregression model; GARCH model; non-parametric noise; system load |
Subjects: | C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C46 - Specific Distributions ; Specific Statistics Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy > Q47 - Energy Forecasting C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes |
Item ID: | 21299 |
Depositing User: | Rafal Weron |
Date Deposited: | 16 Mar 2010 13:07 |
Last Modified: | 08 Oct 2019 10:21 |
References: | Bottazzi G., Sapio S., Secchi A. (2005), Some Statistical Investigations on the Nature and Dynamics of Electricity Prices, Physica, A, 355, 54–61. Bunn D. W. (2000), Forecasting Loads and Prices in Competitive Power Markets, Proceedings of the IEEE, 88(2), 163–169. Bunn D. W. (ed.) (2004), Modelling Prices in Competitive Electricity Markets, New York: Wiley. Cao R., Hart J. D., Saavedra A. (2003), Nonparametric Maximum Likelihood Estimators for AR and MA Time Series, Journal of Statistical Computation and Simulation, 73(5), 347–360. Carr P., Geman H., Madan D. B., Yor M. (2002), The Fine Structure of Asset Returns: An Empirical Investigation, Journal of Business, 75, 305–332. Christoffersen P., Diebold F.X. (2000), How Relevant Is Volatility Forecasting for Financial Risk Management, Review of Economics and Statistics, 82, 12–22. Conejo A. J., Contreras J., Espinola R., Plazas M. A. (2005), Forecasting Electricity Prices for a Day-Ahead Pool-Based Electric Energy Market, International Journal of Forecasting, 21(3), 435–462. Contreras J., Espinola R., Nogales F. J., Conejo A. J. (2003), ARIMA Models to Predict Next-Day Electricity Prices, IEEE Transactions on Power Systems, 18(3), 1014–1020. Engle R. F. (1982), Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation, Econometrica, 50, 987–1007. Jones M. C., Marron J. S., Sheather S. J. (1996), A Brief Survey of Bandwidth Selection for Density Estimation, Journal of the American Statistical Association, 91, 401–407. Hamilton, J. (1994) Time Series Analysis, Princeton: Princeton University Press. Hsieh D. A., Manski C. F. (1987), Monte Carlo Evidence on Adaptive Maximum Likelihood Estimation of a Regression, Annals of Statistics, 15, 541–551. Misiorek A., Trück S., Weron R. (2006), Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models, Studies in Nonlinear Dynamics and Econometrics, 10(3), Art. 2. Rachev S., Mittnik S. (2000), Stable Paretian Models in Finance, New York: Wiley. Weron R. (2004), Computationally Intensive Value at Risk Calculations, in: Gentle J. E., Härdle W., Mori Y., (eds.), Handbook of Computational Statistics: Concepts and Methods, Springer, 911–950. Weron R. (2006), Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach, New York: Wiley. See also: http://www.im.pwr.wroc.pl/~rweron/MFE.html Weron R., Misiorek A. (2007), Heavy Tails and Electricity Prices: Do Time Series Models with Non-Gaussian Noise Forecast Better than Their Gaussian Counterparts?, Prace Naukowe Akademii Ekonomicznej we Wrocławiu, 1076, 472–480. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/21299 |