Enlighten Publications

In this section

It never rains but it pours: modeling the persistence of spikes in electricity prices

Christensen, T.M., Hurn, A.S. and Lindsay, K. (2009) It never rains but it pours: modeling the persistence of spikes in electricity prices. Energy Journal, 30(1), pp. 25-48.

Full text not currently available from Enlighten.

Abstract

During periods of market stress, electricity prices can rise dramatically. This paper treats these abnormal episodes or price spikes as count events and attempts to build a model of the spiking process. By contrast to the existing literature, which either ignores temporal dependence in the spiking process or attempts to model the dependence solely in terms of deterministic variables (like seasonal and day of the week effects), this paper argues that persistence in the spiking process is an important factor in building an effective model. A Poisson autoregressive framework is proposed in which price spikes occur as a result of the latent arrival and survival of system stresses. This formulation captures the salient features of the process adequately, and yields forecasts of price spikes that are superior to those obtained from naïve models that do not account for persistence in the spiking process.

Item Type:	Articles (Other)
Status:	Published
Refereed:	Yes
Glasgow Author(s) Enlighten ID:	Lindsay, Professor Kenneth
Authors:	Christensen, T.M., Hurn, A.S., and Lindsay, K.
Subjects:	H Social Sciences > HA Statistics
College/School:	College of Science and Engineering > School of Mathematics and Statistics > Mathematics
Journal Name:	Energy Journal
Publisher:	International Association for Energy Economics
ISSN:	0195-6574
ISSN (Online):	1944-9089

University Staff: Request a correction | Enlighten Editors: Update this record

References

Al-Osh, M.A. and A.A. Alzaid (1987). “First-order integer valued autoregressive (INAR(1)) process”, Journal of Time Series Analysis 8: 261–275. Barlow, M.T. (2002). “A diffusion model for electricity prices”, Mathematical Finance 12: 287–298. Becker, R., A.S. Hurn and V. Pavlov (2007). “Modeling spikes in electricity prices”, Economic Record 83: 371–382. Bierbrauer, M., C. Menn, S.T. Rachev and S. Trück (2007). “Spot and derivative pricing in the EEX power market”, Journal of Banking and Finance 31: 3462–3485. Breunig, R., S. Najarian and A.R. Pagan (2003). “Specification testing of Markov switching models”, Oxford Bulletin of Economics and Statistics 65: 703–725. Brillinger, D. R. (1975). “Statistical inference for stationary point processes”, in Stochastic Processes and Related Topics, (Vol.1.), Puri, M.I. (Ed), Academic Press: New York. pp. 55–79. Bunn, D.W. and N. Karakatsani (2003). “Forecasting electricity prices”, Working Paper, London Business School. Burger, M., B. Klar, A. Mueller and G. Schindlmayr (2003). “A spot market model for pricing derivatives in electricity markets”, Quantitative Finance 4: 109–122. Byström, H.N.E. (2005). “Extreme value theory and extremely large electricity price changes”, International Review of Economics and Finance 14: 41–55. Cartea, Á. and M.G. Figueroa (2005). “Pricing in electricity markets: A mean reverting jump diffusion model with seasonality”, Applied Mathematical Finance 12: 313–335. Contreras, J., R. Espínola, F.J. Nogales and A.J. Conejo (2003). “ARIMA models to predict next-day electricity prices”, IEEE Transactions on Power Systems 18: 1014–1020. Cox, D.R. and P.A.W. Lewis (1972). “Multivariate point processes”, in Proceedings of the 6th Berkeley Symposium on Mathematics, Statistics and Probability, (Vol. 2), Le Cam, L., Neyman, J. and Scott, E.L. (Eds), University of California Press, Berkeley. pp 401–448. Crespo Cuaresma, J., J. Hlouskova, C. Kossmeier and M. Obersteiner (2004). “Forecasting electricity spot-prices using linear univariate time-series models”, Applied Energy 77: 87–106. de Jong, C. and R. Huisman. (2002). “Option formulas for mean-reverting power prices with spikes”, Working Paper, Erasmus Research Institute of Management Report Series Research in Management, Erasmus Universiteit Rotterdam. de Jong, C. (2006). “The nature of power spikes: A regime-switching approach”, Studies in Nonlinear Dynamics and Econometrics, 10. Escribano, Á, J.I. Peña and P. Villaplana (2002). “Modeling electricity prices: International evidence”,Working Paper 02-27, Universidad Carlos III de Madrid. Freeland, R.K. and B.P.M. McCabe (2004a). “Analysis of low count time series data by Poisson autoregression”, Journal of Time Series Analysis 25: 701–722. Freeland, R.K. and B.P.M. McCabe (2004b). “Forecasting discrete valued low count time series”, International Journal of Forecasting 20: 427–434. Garcia, R.C., J. Contreras, M. van Akkeren and J.B.C. Garcia (2005). “A GARCH forecasting model to predict day-ahead electricity prices”, IEEE Transactions on Power Systems 20: 867–874. Geman, H. and A. Roncorni (2006). “Understanding the fine structure of electricity prices”, Journal of Business 79: 1225–1261. Greene, W.H. (1994). “Accounting for excess zeros and sample selection in Poisson and negative binomial regression models”, Working Paper, Stern School of Business, New York University. Harding, D. and A.R. Pagan (2006). “The econometric analysis of constructed binary time series”, Working Paper, National Centre for Econometric Research. Huisman, R. and R. Mahieu (2003). “Regime jumps in electricity prices”, Energy Economics 25: 425–434. Kanamura, T. and K. Ōhashi (2007). “On transition probabilities of regime switching in electricity prices”, Energy Economics, 30: 1158–1172. Knittel, C.R. and M.R. Roberts (2005). “An empirical examination of restructured electricity prices”, Energy Economics 27: 791–817. Kosater, P. and K. Mosler (2006). “Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices”, Applied Energy 83: 943–958. Lindsay, K.A. and J.R. Rosenberg (2007). “Interpretation and application of measures of association between neuronal signals”, Working Paper, University of Glasgow. Lucia, J.J. and E.S. Schwartz (2002). “Electricity prices and power derivatives: Evidence from the Nordic power exchange”, Review of Derivatives Research 5: 5–50. McCabe, B.P.M. and G.M. Martin (2005). “Bayesian predictions of low count time series”, International Journal of Forecasting 21: 315–330. McKenzie, E. (1988). “Some ARMA models for dependent sequences of Poisson counts”, Advances in Applied Probability 20: 822–35. Misiorek, A., S. Trück and R. Weron (2006). “Point and interval forecasting of spot electricity prices: Linear vs. non-linear time series models”, Studies in Nonlinear Dynamics and Econometrics, 10. Mount, T.D., Y. Ning and X. Cai (2006). “Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters”, Energy Economics, 28: 62–80. Ramanathan, R., R.F. Engle, C.W.J. Granger, F. Vahid-Araghi. and C. Brace (1997). “Short-run forecasts of electricity loads and peaks”, International Journal of Forecasting, 13, 161–174. Swider, D.J. and C. Weber (2007). “Extended ARMA models for estimating price developments on day-ahead electricity markets”, Electric Power Systems Research 77: 583–593. Taylor, J.W. and R. Buizza (2003). “Using weather ensemble predictions in electricity demand forecasting”, International Journal of Forecasting 19: 57–70. Weron, R., M. Bierbrauer and S. Trück (2004). “Modeling electricity prices: jump diffusion and regime switching”, Physica A 336: 39–48.

Deposit and Record Details

ID Code:	38582
Depositing User:	Professor Kenneth Lindsay
Datestamp:	29 Sep 2010 11:38
Last Modified:	22 Sep 2021 10:45
Date of first online publication:	2009