Skip to main content
SpringerLink
Log in

Regression tree model versus Markov regime switching: a comparison for electricity spot price modelling and forecasting

  • Review
  • Published:
Operational Research Aims and scope Submit manuscript

Abstract

This paper models electricity spot prices using a Markov regime switching (MRS) model and regression trees (RT). MRS models offer the possibility to divide the time series into different regimes with different underlying processes. RT is a data driven technique aiming in finding a classifier that performs an average guessing for the response variable in question, which is the short term electricity spot price. We use a dataset consisting of average day ahead spot electricity prices for the MRS model. Then, we use hourly data to build the RT model. The empirical evidence supports that the regression tree approach outperforms the MRS model. We also compare the forecasting accuracy of the regression tree model by incorporating different predictors sets for electricity prices and logarithmic electricity prices. We find that a model with 11 predictors, accounting for logarithmic prices fits best our data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. Reconstructing means to vary the scale and position parameters. Scale values determine the degree to which the wavelet is compressed or stretched. Low scale values compress the wavelet and correlate better with high frequencies. The low scale coefficients represent the fine-scale features of a time series. High scale values stretch the wavelet and correlate better with the low frequency content. The high scale coefficients represent the coarse-scale features.

  2. Source: U.S. Energy Information Administration, Annual Energy Review, 2011.

  3. The Henry hub is a distribution hub on the natural gas pipeline system. Due to its importance, it lends its name to the pricing point for natural gas futures contracts traded on the New York Mercantile Exchange (NYMEX) and the OTC swaps traded on Intercontinental Exchange (ICE).

References

  • Aggarwal SK, Saini LM, Kumar A (2009) Day-ahead price forecasting in ontario electricity market using variable-segmented support vector machine-based model. Electric Power Compon Syst 37(5):495–516

    Article  Google Scholar 

  • Barlow M (2002) A diffusion model for electricity prices. Math Finance 12:287–298

    Article  Google Scholar 

  • Becker R, Hurn S, Pavlov V (2007) Modelling spikes in electricity prices. Econ Record 83:371–382

    Article  Google Scholar 

  • Bernard J-T, Khalaf L, Kichian M, Mcmahon S (2008) Forecasting commodity prices: Garch, jumps, and mean reversion. J Forecast 27(4):279–291

    Article  Google Scholar 

  • Bessec M, Bouabdallah O (2005) What causes the forecasting failure of markov-switching models? A monte carlo study. Stud Nonlinear Dyn Econom 9(2):1–24

  • Bessembinder H, Lemmon ML (2002) Equilibrium pricing and optimal hedging in electricity forward markets. J Finance 57(3):1347–1382

    Article  Google Scholar 

  • Bierbrauer A, Weron R, Truck C (2004) Modeling electricity prices: jump difusion and regime switching. Physica A 336:39–48

  • Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth and Brooks, Monterey

    Google Scholar 

  • Cartea Figueroa (2005) Pricing in electricity markets: a mean reverting jump diffusion model with seasonality. Appl Math Finance 12:313–335

    Article  Google Scholar 

  • Cartea Villaplana (2008) Spot price modeling and the valuation of electricity forward contracts: the role of demand and capacity. J Bank Finance 32:2502–2519

    Article  Google Scholar 

  • Catalao J, Mariano S, Mendes V, Ferreira L (2007) Short-term electricity prices forecasting in a competitive market: a neural network approach. Electric Power Syst Res 77:1297–1304

    Article  Google Scholar 

  • Clewlow L, Strickland C (2000) Energy derivatives, pricing and risk management. Lacima Publications, London

    Google Scholar 

  • De Jong C (2006) The nature of power spikes: a regime-switch approach. Stud Nonlinear Dyn Econom 10(3), Art No 3. doi:10.2202/1558-3708.1361

  • Deng (ed) (2000) Pricing electricity derivatives under alternative stochastic models and its applications. In: Proceedings of the 33rd Hawaii international conference on system sciences

  • Dimitras A, Siriopoulos C (2006) Modelling and decision support in financial markets. Oper Res 6(2):83–84

    Google Scholar 

  • Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press, Princeton

    Google Scholar 

  • Ethier and Mount (1998) Estimating the volatility of spot prices in restructured electricity markets and the implications for option values. Cornell University Working Paper

  • Eydeland A, Geman H (1999) Energy modelling and management of uncertainty. Risk Books, New York

    Google Scholar 

  • Gao C, Bompard E, Napoli R, Cheng H (2007) Price forecast in the competitive electricity market by support vector machine. Phys A Stat Mech Appl 382(1):98–113

    Article  Google Scholar 

  • Geman Roncoroni (2006) Understanding the fine structure of electricity prices. J Bus 79:1225–1261

    Article  Google Scholar 

  • Georgilakis P (2006) Artificial intelligenc to electricity price forecasting problem. Appl Artif Intel 19:707–727

    Google Scholar 

  • Hamilton JD (1998) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357–384

    Article  Google Scholar 

  • Huisman (2008) The influence of temperature on spike probability in day-ahead power prices. Energy Econ 30:2697–2704

    Article  Google Scholar 

  • Janczura J, Weron R (2010) An empirical comparison of alternate regime-switching models for electricity spot prices. Energy Econ 32:1059–1073

    Article  Google Scholar 

  • Jong D, Huisman (2003) Option pricing for power prices with spikes. Energy Power Risk Manage 7:12–16

    Google Scholar 

  • Kaminski V (1997) The challenge of pricing and risk managing electricity derivatives. US Power Market 3:149–171

    Google Scholar 

  • Kanamura T, Ohashi K (2008) On transition probabilities of regime switching in electricity prices. Energy Econ 30(3):1158–1172

    Article  Google Scholar 

  • Karakatsani NV, Bunn DW (2008) Intra-day and regime-switching dynamics in electricity price formation. Energy Econ 30(4):1776–1797

    Article  Google Scholar 

  • Mahieu Huisman (2003) Regime jumps in electricity prices. Energy Econ 5:425–434

    Google Scholar 

  • Mari C, Tondini D (2010) Regime switches induced by supply and demand equilibrium: a model for power-price dynamics. Physica A 389:4819–4827

    Article  Google Scholar 

  • Misiorek A, Trueck S, Weron R (2006) Point and interval forecasting of spot electricity prices: linear vs. non-linear time series models. Stud Nonlinear Dyn Econom 10(3):1–36

  • Mount (2006) Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters. Energy Econ 28:62–80

    Article  Google Scholar 

  • Pilipovic D (1998) Energy risk: valuing and managing energy derivatives. McGraw-Hill, New York

    Google Scholar 

  • Rambharat (2005) A threshold autoregressive model for wholesale electricity prices. Appl Stat 54:287–299 (part 2)

  • Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Schwartz Lucia (2002) Electricity prices and power derivatives: evidence from the nordic power exchange. Rev Deriv 5:5–50

    Article  Google Scholar 

  • Schwartz ES (1997) The stochastic behavior of commodity prices: implications for valuation and hedging. J Finance 52(3):923–973

    Article  Google Scholar 

  • Silva S, Fidalgo J, Fontes D (2011) A simulation based decision aid tool for setting regulation of energy grids with distributed generation. Oper Res 11(1):41–57

    Google Scholar 

  • Thomaidis F, Konidari P, Mavrakis D (2008) The wholesale natural gas market prospects in the energy community treaty countries. Oper Res 8(1):63–75

    Google Scholar 

  • Thomas LC (2000) A survey of credit and behavioural scoring: forecasting nancial risk of lending to consumers. Int J Forecast 16:149–172

    Article  Google Scholar 

  • Treslong ABV, Huisman R (2010) A comment on: storage and the electricity forward premium. Energy Econ 32(2):321–324

    Article  Google Scholar 

  • Trevor H, Robert T, Jerom F (2003) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin

    Google Scholar 

  • Trueck S, Weron R, Wolff R (2007) Outlier treatment and robust approaches for modeling electricity spot prices. Mpra paper. University Library of Munich, Germany

    Google Scholar 

  • Tsagkanos A, Georgopoulos A, Siriopoulos C (2007) Predicting Greek mergers and acquisitions: a new approach. Int J Financial Serv Manage 2(4):289–303

  • Tsagkanos A, Koumanakos E, Georgopoulos A, Siriopoulos C (2012) Prediction of Greek takeover targets via bootstrapping on mixed logit model. Rev Acc Finance 11(3):315–334

  • Voronin S, Partanen J (2012) A hybrid electricity price forecasting model for the finnish electricity spot market. In: The 32st annual international symposium on forecasting

  • Weron R (2006) Modeling and forecasting electricity loads and Prices: a statistical approach. HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology

  • Weron R, Misiorek A (2008) Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. Int J Forecast 24:744–763

    Article  Google Scholar 

  • Wu W, Zhou J, Mo L, Zhu C (2006) Forecasting electricity market price spikes based on bayesian expert with support vector machines. Adv Data Mining Appl 4093:205–212

    Article  Google Scholar 

  • Yamin H, Shahidehpour S, Li Z (2004) Adaptive short-term electricity price forecasting using artificial neural networks in the restructured power markets. Int J Electr Power Energy Syst 26(8):571–581

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Business Administration, Business School, University of the Aegean, 8 Michalon Str, 82100, Chios, Greece

    Aristeidis Samitas & Aggelos Armenatzoglou

Authors
  1. Aristeidis Samitas
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aggelos Armenatzoglou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aristeidis Samitas.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Samitas, A., Armenatzoglou, A. Regression tree model versus Markov regime switching: a comparison for electricity spot price modelling and forecasting. Oper Res Int J 14, 319–340 (2014). https://doi.org/10.1007/s12351-014-0149-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12351-014-0149-6

Keywords

Search

Navigation