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

详细信息    查看全文

• 作者：Aristeidis Samitas (1)
  Aggelos Armenatzoglou (1)
  
• 关键词：Energy markets ; Electricity prices ; Parametric ; Non parametric ; Forecast performance
• 刊名：Operational Research
• 出版年：2014
• 出版时间：October 2014
• 年：2014
• 卷：14
• 期：3
• 页码：319-340
• 全文大小：1,075 KB
• 参考文献：1. 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鈥?16 CrossRef
  2. Barlow M (2002) A diffusion model for electricity prices. Math Finance 12:287鈥?98 CrossRef
  3. Becker R, Hurn S, Pavlov V (2007) Modelling spikes in electricity prices. Econ Record 83:371鈥?82 CrossRef
  4. Bernard J-T, Khalaf L, Kichian M, Mcmahon S (2008) Forecasting commodity prices: Garch, jumps, and mean reversion. J Forecast 27(4):279鈥?91 CrossRef
  5. 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鈥?4
  6. Bessembinder H, Lemmon ML (2002) Equilibrium pricing and optimal hedging in electricity forward markets. J Finance 57(3):1347鈥?382 CrossRef
  7. Bierbrauer A, Weron R, Truck C (2004) Modeling electricity prices: jump difusion and regime switching. Physica A 336:39鈥?8
  8. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Wadsworth and Brooks, Monterey
  9. Cartea Figueroa (2005) Pricing in electricity markets: a mean reverting jump diffusion model with seasonality. Appl Math Finance 12:313鈥?35 CrossRef
  10. Cartea Villaplana (2008) Spot price modeling and the valuation of electricity forward contracts: the role of demand and capacity. J Bank Finance 32:2502鈥?519 CrossRef
  11. 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鈥?304 CrossRef
  12. Clewlow L, Strickland C (2000) Energy derivatives, pricing and risk management. Lacima Publications, London
  13. 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
  14. Deng (ed) (2000) Pricing electricity derivatives under alternative stochastic models and its applications. In: Proceedings of the 33rd Hawaii international conference on system sciences
  15. Dimitras A, Siriopoulos C (2006) Modelling and decision support in financial markets. Oper Res 6(2):83鈥?4
  16. Dixit AK, Pindyck RS (1994) Investment under uncertainty. Princeton University Press, Princeton
  17. Ethier and Mount (1998) Estimating the volatility of spot prices in restructured electricity markets and the implications for option values. Cornell University Working Paper
  18. Eydeland A, Geman H (1999) Energy modelling and management of uncertainty. Risk Books, New York
  19. 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鈥?13 CrossRef
  20. Geman Roncoroni (2006) Understanding the fine structure of electricity prices. J Bus 79:1225鈥?261 CrossRef
  21. Georgilakis P (2006) Artificial intelligenc to electricity price forecasting problem. Appl Artif Intel 19:707鈥?27
  22. Hamilton JD (1998) A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57:357鈥?84 CrossRef
  23. Huisman (2008) The influence of temperature on spike probability in day-ahead power prices. Energy Econ 30:2697鈥?704 CrossRef
  24. Janczura J, Weron R (2010) An empirical comparison of alternate regime-switching models for electricity spot prices. Energy Econ 32:1059鈥?073 CrossRef
  25. Jong D, Huisman (2003) Option pricing for power prices with spikes. Energy Power Risk Manage 7:12鈥?6
  26. Kaminski V (1997) The challenge of pricing and risk managing electricity derivatives. US Power Market 3:149鈥?71
  27. Kanamura T, Ohashi K (2008) On transition probabilities of regime switching in electricity prices. Energy Econ 30(3):1158鈥?172 CrossRef
  28. Karakatsani NV, Bunn DW (2008) Intra-day and regime-switching dynamics in electricity price formation. Energy Econ 30(4):1776鈥?797 CrossRef
  29. Mahieu Huisman (2003) Regime jumps in electricity prices. Energy Econ 5:425鈥?34
  30. Mari C, Tondini D (2010) Regime switches induced by supply and demand equilibrium: a model for power-price dynamics. Physica A 389:4819鈥?827 CrossRef
  31. 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鈥?6
  32. Mount (2006) Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters. Energy Econ 28:62鈥?0 CrossRef
  33. Pilipovic D (1998) Energy risk: valuing and managing energy derivatives. McGraw-Hill, New York
  34. Rambharat (2005) A threshold autoregressive model for wholesale electricity prices. Appl Stat 54:287鈥?99 (part 2)
  35. Ripley BD (1996) Pattern recognition and neural networks. Cambridge University Press, Cambridge CrossRef
  36. Schwartz Lucia (2002) Electricity prices and power derivatives: evidence from the nordic power exchange. Rev Deriv 5:5鈥?0 CrossRef
  37. Schwartz ES (1997) The stochastic behavior of commodity prices: implications for valuation and hedging. J Finance 52(3):923鈥?73 CrossRef
  38. 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鈥?7
  39. Thomaidis F, Konidari P, Mavrakis D (2008) The wholesale natural gas market prospects in the energy community treaty countries. Oper Res 8(1):63鈥?5
  40. Thomas LC (2000) A survey of credit and behavioural scoring: forecasting nancial risk of lending to consumers. Int J Forecast 16:149鈥?72 CrossRef
  41. Treslong ABV, Huisman R (2010) A comment on: storage and the electricity forward premium. Energy Econ 32(2):321鈥?24 CrossRef
  42. Trevor H, Robert T, Jerom F (2003) The elements of statistical learning: data mining, inference, and prediction. Springer, Berlin
  43. Trueck S, Weron R, Wolff R (2007) Outlier treatment and robust approaches for modeling electricity spot prices. Mpra paper. University Library of Munich, Germany
  44. Tsagkanos A, Georgopoulos A, Siriopoulos C (2007) Predicting Greek mergers and acquisitions: a new approach. Int J Financial Serv Manage 2(4):289鈥?03
  45. 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鈥?34
  46. 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
  47. Weron R (2006) Modeling and forecasting electricity loads and Prices: a statistical approach. HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology
  48. Weron R, Misiorek A (2008) Forecasting spot electricity prices: a comparison of parametric and semiparametric time series models. Int J Forecast 24:744鈥?63 CrossRef
  49. 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鈥?12 CrossRef
  50. 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鈥?81 CrossRef
  
• 作者单位：Aristeidis Samitas (1)
  Aggelos Armenatzoglou (1)
  
  1. Department of Business Administration, Business School, University of the Aegean, 8 Michalon Str, 82100, Chios, Greece
  
• ISSN：1866-1505

文摘

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.