Forecasting spikes in electricity prices

In many electricity markets, retailers purchase electricity at an unregulated spot price and sell to consumers at a heavily regulated price. Consequently, the occurrence of spikes in the spot electricity price represents a major source of risk for retailers, and the forecasting of these price spikes is important for effective risk management. Traditional approaches to modelling electricity prices have aimed to predict the trajectory of spot prices. In contrast, this paper focuses on the prediction of price spikes. The time series of price spikes is treated as a discrete-time point process, and a nonlinear variant of the autoregressive conditional hazard model is used to model this process. The model is estimated using half-hourly data from the Australian electricity market for the period 1 March 2001 to 30 June 2007. One-step-ahead forecasts of the probability of a price spike are then generated for each half hour in the forecast period, 1 July 2007 to 30 September 2007. The forecasting performance of the model is then evaluated against a benchmark that is consistent with the assumptions of commonly-used electricity pricing models.

[1]  Robert F. Engle,et al.  Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model , 1997 .

[2]  R. Huisman,et al.  Hourly Electricity Prices in Day-Ahead Markets , 2006 .

[3]  Hans Byström,et al.  Extreme Value Theory and Extremely Large Electricity Price Changes , 2005 .

[4]  D. J. Swider,et al.  Extended ARMA models for estimating price developments on day-ahead electricity markets , 2007 .

[5]  S. Rachev,et al.  Spot and Derivative Pricing in the EEX Power Market , 2007 .

[6]  Timothy D. Mount,et al.  Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters , 2006 .

[7]  L. Bauwens,et al.  The Logarithmic Acd Model: An Application to the Bid-Ask Quote Process of Three NYSE Stocks , 2000 .

[8]  K. Mosler,et al.  Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices , 2006 .

[9]  R. Huisman,et al.  Regime Jumps in Electricity Prices , 2001 .

[10]  Marco van Akkeren,et al.  A GARCH forecasting model to predict day-ahead electricity prices , 2005, IEEE Transactions on Power Systems.

[11]  L. Bauwens,et al.  The stochastic conditional duration model: a latent variable model for the analysis of financial durations , 2004 .

[12]  Jeffrey R. Russell,et al.  A Discrete-State Continuous-Time Model of Financial Transactions Prices and Times , 2005 .

[13]  Kenneth A. Lindsay,et al.  It never rains but it pours: Modelling the persistence of spikes in electricity prices , 2009 .

[14]  Joachim Grammig,et al.  A family of autoregressive conditional duration models , 2006 .

[15]  Q. Vuong.  Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses , 1989 .

[16]  M. Barlow.  A DIFFUSION MODEL FOR ELECTRICITY PRICES , 2002 .

[17]  J. Contreras,et al.  ARIMA models to predict next-day electricity prices , 2002 .

[18]  R. Weron,et al.  Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models , 2006 .

[19]  Dingan Feng,et al.  Stochastic Conditional Duration Models with ''Leverage Effect'' for Financial Transaction Data , 2004 .

[20]  James D. Hamilton,et al.  A Model for the Federal Funds Rate Target , 1999 .

[21]  Takashi Kanamura,et al.  On Transition Probabilities of Regime-Switching in Electricity Prices , 2004 .

[22]  Á. Cartea,et al.  Pricing in Electricity Markets: A Mean Reverting Jump Diffusion Model with Seasonality , 2005 .

[23]  Ruey S. Tsay,et al.  A nonlinear autoregressive conditional duration model with applications to financial transaction data , 2001 .

[24]  Christopher R. Knittel,et al.  An empirical examination of restructured electricity prices , 2005 .

[25]  K. F. Chan,et al.  Using extreme value theory to measure value-at-risk for daily electricity spot prices , 2006 .

[26]  Ralf Becker,et al.  Modeling Spikes in Electricity Prices , 2007 .

[27]  R. Ramanathan,et al.  Short-run forecasts of electricity loads and peaks , 1997 .

[28]  Philip Gray,et al.  A New Approach to Characterizing and Forecasting Electricity Price Volatility , 2008 .

[29]  Glenn D. Rudebusch,et al.  Forecasting Recessions: The Puzzle of the Enduring Power of the Yield Curve , 2007 .

[30]  E. Anderson,et al.  Forward contracts in electricity markets: The Australian experience , 2007 .

[31]  M. Obersteiner,et al.  Forecasting electricity spot-prices using linear univariate time-series models , 2004 .

[32]  Anastasios Panagiotelis,et al.  Bayesian Density Forecasting of Intraday Electricity Prices using Multivariate Skew t Distributions , 2008 .

[33]  Jeffrey R. Russell,et al.  Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data , 1998 .

[34]  Zhao Yang Dong,et al.  Electricity market price spike forecasting and decision making , 2007 .

[35]  Cyriel de Jong.  The Nature of Power Spikes: a regime-switch approach , 2005 .

[36]  Robert F. Engle,et al.  The Econometrics of Ultra-High Frequency Data , 1996 .

[37]  R. Weron,et al.  Modelling Electricity Prices: Jump Diffusion and Regime Switching , 2004 .

[38]  H. Geman,et al.  Understanding the Fine Structure of Electricity Prices , 2004 .