IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v142y2018icp1083-1103.html
   My bibliography  Save this article

Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model

Author

Listed:
  • Papaioannou, George P.
  • Dikaiakos, Christos
  • Dagoumas, Athanasios S.
  • Dramountanis, Anargyros
  • Papaioannou, Panagiotis G.

Abstract

This work aims to detect the impact of fundamentals and regulatory reforms on the Greek Wholesale Electricity Market, applying SARMAX/GARCH models. The System Marginal Price (SMP) is considered a stochastic, nonlinear process, reflecting not only the effects of endogenous/fundamental market factors but also the effects of exogenous variables including regulatory reforms, which also affect the market dynamics. To capture the dynamics of the conditional mean and volatility of SMP, a number of SARMAX/GARCH models have been estimated using as regressors an extensive set of fundamental factors in the Greek Electricity Market (GEM), as well as dummy variables that mimic the history of Regulator's interventions. The best-found model captures adequately the dependency of the spot price to the regulatory reforms. The findings reassure the typical sign and the magnitude of the effect of fundamentals, and detects successfully the impacts of the reforms. The most interesting finding is that the GEM does not exhibit asymmetries or leverage effect, in the volatility of its wholesale price, as the most European markets do. The outcome of this paper can be useful to a wide variety of GEM's participants and specifically to the decision makers in GEM.

Suggested Citation

  • Papaioannou, George P. & Dikaiakos, Christos & Dagoumas, Athanasios S. & Dramountanis, Anargyros & Papaioannou, Panagiotis G., 2018. "Detecting the impact of fundamentals and regulatory reforms on the Greek wholesale electricity market using a SARMAX/GARCH model," Energy, Elsevier, vol. 142(C), pages 1083-1103.
  • Handle: RePEc:eee:energy:v:142:y:2018:i:c:p:1083-1103
    DOI: 10.1016/j.energy.2017.10.064
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544217317693
    Download Restriction: Full text for ScienceDirect subscribers only

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(01), pages 122-150, February.
    3. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    4. Bowden, Nicholas & Payne, James E., 2008. "Short term forecasting of electricity prices for MISO hubs: Evidence from ARIMA-EGARCH models," Energy Economics, Elsevier, vol. 30(6), pages 3186-3197, November.
    5. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    6. Diongue, Abdou Kâ & Guégan, Dominique & Vignal, Bertrand, 2009. "Forecasting electricity spot market prices with a k-factor GIGARCH process," Applied Energy, Elsevier, vol. 86(4), pages 505-510, April.
    7. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    8. Apostolos Serletis, 2012. "Quantitative and Empirical Analysis of Energy Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8624, January.
    9. Kalantzis, Fotis & Sakellaris, Kostis, 2012. "Investigating the Impact of the Greek Electricity Market Reforms on its Day-Ahead Market Prices," MPRA Paper 37794, University Library of Munich, Germany.
    10. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    11. Petrella, Andrea & Sapio, Alessandro, 2012. "Assessing the impact of forward trading, retail liberalization, and white certificates on the Italian wholesale electricity prices," Energy Policy, Elsevier, vol. 40(C), pages 307-317.
    12. Buhlmann, Peter & McNeil, Alexander J., 2002. "An algorithm for nonparametric GARCH modelling," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 665-683, October.
    13. repec:hal:journl:halshs-00307606 is not listed on IDEAS
    14. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    15. Hickey, Emily & Loomis, David G. & Mohammadi, Hassan, 2012. "Forecasting hourly electricity prices using ARMAX–GARCH models: An application to MISO hubs," Energy Economics, Elsevier, vol. 34(1), pages 307-315.
    16. George P. Papaioannou & Christos Dikaiakos & George Evangelidis & Panagiotis G. Papaioannou & Dionysios S. Georgiadis, 2015. "Co-Movement Analysis of Italian and Greek Electricity Market Wholesale Prices by Using a Wavelet Approach," Energies, MDPI, Open Access Journal, vol. 8(10), pages 1-30, October.
    17. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    18. Lin, Sharon Xiaowen & Tamvakis, Michael N., 2001. "Spillover effects in energy futures markets," Energy Economics, Elsevier, vol. 23(1), pages 43-56, January.
    19. Karakatsani Nektaria V & Bunn Derek W., 2010. "Fundamental and Behavioural Drivers of Electricity Price Volatility," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 14(4), pages 1-42, September.
    20. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601.
    21. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    22. Liu, Heping & Shi, Jing, 2013. "Applying ARMA–GARCH approaches to forecasting short-term electricity prices," Energy Economics, Elsevier, vol. 37(C), pages 152-166.
    23. M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298.
    24. Merton, Robert C., 1980. "On estimating the expected return on the market : An exploratory investigation," Journal of Financial Economics, Elsevier, vol. 8(4), pages 323-361, December.
    25. Weron, Rafał, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," International Journal of Forecasting, Elsevier, vol. 30(4), pages 1030-1081.
    26. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    27. Theodorou, Petros & Karyampas, Dimitrios, 2008. "Modeling the return and volatility of the Greek electricity marginal system price," Energy Policy, Elsevier, vol. 36(7), pages 2601-2609, July.
    28. Lester Hadsell & Hany A. Shawky, 2006. "Electricity Price Volatility and the Marginal Cost of Congestion: An Empirical Study of Peak Hours on the NYISO Market, 2001-2004," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 157-180.
    29. repec:kap:iaecre:v:13:y:2007:i:4:p:415-432 is not listed on IDEAS
    30. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    31. George P. Papaioannou & Christos Dikaiakos & Anargyros Dramountanis & Panagiotis G. Papaioannou, 2016. "Analysis and Modeling for Short- to Medium-Term Load Forecasting Using a Hybrid Manifold Learning Principal Component Model and Comparison with Classical Statistical Models (SARIMAX, Exponential Smoot," Energies, MDPI, Open Access Journal, vol. 9(8), pages 1-40, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. repec:eee:energy:v:162:y:2018:i:c:p:776-786 is not listed on IDEAS

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:142:y:2018:i:c:p:1083-1103. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu). General contact details of provider: http://www.journals.elsevier.com/energy .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.