IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2017-02-33.html
   My bibliography  Save this article

Comparing Iranian and Spanish Electricity Markets with Nonlinear Time Series

Author

Listed:
  • Hajar Nasrazadani

    (Department of Statistics and Operations Research, Universitat Polit cnica de Catalunya, Barcelona, Spain,)

  • Maria Pilar Mu oz Gracia

    (Department of Statistics and Operations Research, Universitat Polit cnica de Catalunya, Barcelona, Spain)

Abstract

Electricity market analysis is useful for accessing strategic market information in order to set energy policy. According to recent interpretations of the Article 44 of the Iranian Laws, the Iranian electricity market is to become a free market. Mechanisms that were implemented in the Spanish electricity market - a free market - provide a versatile benchmark to employ time series modeling approach to compare Iran and Spain s electricity markets via price and load time series as two main indices. Here, we develop linear (autoregressive integrated moving average [MA]), heteroskedastic (autoregressive MA model [ARMA]-generalized autoregressive conditional heteroskedastic [GARCH]), and nonlinear time series models to model the Iranian/Spanish electricity market for price and load time series indices. We further utilize the conditional variance to propose the ARMA-TGARCH model as the best suited model for the Iranian electricity market price. We employ our models and time series analysis to forecast and question the status of the Iranian market structure as a free market.

Suggested Citation

  • Hajar Nasrazadani & Maria Pilar Mu oz Gracia, 2017. "Comparing Iranian and Spanish Electricity Markets with Nonlinear Time Series," International Journal of Energy Economics and Policy, Econjournals, vol. 7(2), pages 262-286.
    • Handle: RePEc:eco:journ2:2017-02-33
    as

    Download full text from publisher

    File URL: http://www.econjournals.com/index.php/ijeep/article/download/4226/2827
    Download Restriction: no
    File URL: http://www.econjournals.com/index.php/ijeep/article/view/4226/2827
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Afshar, K. & Bigdeli, N., 2011. "Data analysis and short term load forecasting in Iran electricity market using singular spectral analysis (SSA)," Energy, Elsevier, vol. 36(5), pages 2620-2627.
    2. Woo, Chi-Keung & Lloyd, Debra & Tishler, Asher, 2003. "Electricity market reform failures: UK, Norway, Alberta and California," Energy Policy, Elsevier, vol. 31(11), pages 1103-1115, September.
    3. Mazarei, Adnan, Jr, 1996. "The Iranian Economy under the Islamic Republic: Institutional Change and Macroeconomic Performance (1979-1990)," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 20(3), pages 289-314, May.
    4. Ketterer, Janina C., 2014. "The impact of wind power generation on the electricity price in Germany," Energy Economics, Elsevier, vol. 44(C), pages 270-280.
    5. Finn, Mary G, 2000. "Perfect Competition and the Effects of Energy Price Increases on Economic Activity," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 32(3), pages 400-416, August.
    6. Mina Mahbub Hossain Author_Email: mahbubfhisrt@gmail.com & Mehdi Rajeb & Mahendran Shitan, 2011. "Time Series Analysis Of The General Index Of Dhaka Stock Exchange In Bangladesh: A Comparative Study Of Garch And Arima Models," 2nd International Conference on Business and Economic Research (2nd ICBER 2011) Proceeding 2011-592, Conference Master Resources.
    7. 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.
    8. Jushan Bai & Serena Ng, 2005. "Tests for Skewness, Kurtosis, and Normality for Time Series Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 49-60, January.
    9. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    10. Yu, Jongmin & Mallory, Mindy L., 2014. "Exchange rate effect on carbon credit price via energy markets," Journal of International Money and Finance, Elsevier, vol. 47(C), pages 145-161.
    11. Conejo, Antonio J. & Contreras, Javier & Espinola, Rosa & Plazas, Miguel A., 2005. "Forecasting electricity prices for a day-ahead pool-based electric energy market," International Journal of Forecasting, Elsevier, vol. 21(3), pages 435-462.
    12. Farzanegan, Mohammad Reza & Markwardt, Gunther, 2009. "The effects of oil price shocks on the Iranian economy," Energy Economics, Elsevier, vol. 31(1), pages 134-151, January.
    13. Moutinho, Victor & Vieira, Joel & Carrizo Moreira, António, 2011. "The crucial relationship among energy commodity prices: Evidence from the Spanish electricity market," Energy Policy, Elsevier, vol. 39(10), pages 5898-5908, October.
    14. Ali Nazemi & Anahita Farsaee, 2014. "Non-Competitive Potential in the Iranian Electricity Market," Proceedings of Economics and Finance Conferences 0401511, International Institute of Social and Economic Sciences.
    15. Asgari, Mohammad Hossein & Monsef, Hassan, 2010. "Market power analysis for the Iranian electricity market," Energy Policy, Elsevier, vol. 38(10), pages 5582-5599, October.
    16. Neelabh Rohan & T. V. Ramanathan, 2013. "Nonparametric estimation of a time-varying GARCH model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 25(1), pages 33-52, March.
    17. Ciarreta, Aitor & Espinosa, Maria Paz & Pizarro-Irizar, Cristina, 2014. "Is green energy expensive? Empirical evidence from the Spanish electricity market," Energy Policy, Elsevier, vol. 69(C), pages 205-215.
    18. M. Pilar Muñoz & M. Dolores Marquez & Lesly M. Acosta, 2007. "Forecasting volatility by means of threshold models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(5), pages 343-363.
    19. Ventosa, Mariano & Baillo, Alvaro & Ramos, Andres & Rivier, Michel, 2005. "Electricity market modeling trends," Energy Policy, Elsevier, vol. 33(7), pages 897-913, May.
    20. Cong, Rong-Gang & Wei, Yi-Ming & Jiao, Jian-Lin & Fan, Ying, 2008. "Relationships between oil price shocks and stock market: An empirical analysis from China," Energy Policy, Elsevier, vol. 36(9), pages 3544-3553, September.
    21. Garcia, Alfredo & Arbelaez, Luis E., 2002. "Market power analysis for the Colombian electricity market," Energy Economics, Elsevier, vol. 24(3), pages 217-229, May.
    22. Bigdeli, N. & Afshar, K., 2009. "Characterization of Iran electricity market indices with pay-as-bid payment mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1577-1592.
    23. Muñoz, M. Pilar & Dickey, David A., 2009. "Are electricity prices affected by the US dollar to Euro exchange rate? The Spanish case," Energy Economics, Elsevier, vol. 31(6), pages 857-866, November.
    24. Rafal Weron, 2006. "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach," HSC Books, Hugo Steinhaus Center, Wroclaw University of Technology, number hsbook0601, December.
    25. Taylor, James W., 2006. "Density forecasting for the efficient balancing of the generation and consumption of electricity," International Journal of Forecasting, Elsevier, vol. 22(4), pages 707-724.
    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. Al Aali-Bujari & Francisco Venegas-Mart nez & Roberto J. Santill n-Salgado, 2018. "On the Stock Market-Electricity Sector Nexus in Latin America: A Dynamic Panel Data Model," International Journal of Energy Economics and Policy, Econjournals, vol. 8(6), pages 148-154.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Auer, Benjamin R., 2016. "How does Germany's green energy policy affect electricity market volatility? An application of conditional autoregressive range models," Energy Policy, Elsevier, vol. 98(C), pages 621-628.
    2. Pape, Christian & Hagemann, Simon & Weber, Christoph, 2016. "Are fundamentals enough? Explaining price variations in the German day-ahead and intraday power market," Energy Economics, Elsevier, vol. 54(C), pages 376-387.
    3. Misiorek Adam & Trueck Stefan & Weron Rafal, 2006. "Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 10(3), pages 1-36, September.
    4. Shadi Tehrani & Jesús Juan & Eduardo Caro, 2022. "Electricity Spot Price Modeling and Forecasting in European Markets," Energies, MDPI, vol. 15(16), pages 1-23, August.
    5. Lang, Korbinian & Auer, Benjamin R., 2020. "The economic and financial properties of crude oil: A review," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
    6. Rafal Weron & Florian Ziel, 2018. "Electricity price forecasting," HSC Research Reports HSC/18/08, Hugo Steinhaus Center, Wroclaw University of Technology.
    7. 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.
    8. Ziel, Florian & Steinert, Rick, 2016. "Electricity price forecasting using sale and purchase curves: The X-Model," Energy Economics, Elsevier, vol. 59(C), pages 435-454.
    9. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    10. Bello, Antonio & Reneses, Javier & Muñoz, Antonio & Delgadillo, Andrés, 2016. "Probabilistic forecasting of hourly electricity prices in the medium-term using spatial interpolation techniques," International Journal of Forecasting, Elsevier, vol. 32(3), pages 966-980.
    11. repec:dui:wpaper:1502 is not listed on IDEAS
    12. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    13. Ioannidis, Filippos & Kosmidou, Kyriaki & Savva, Christos & Theodossiou, Panayiotis, 2021. "Electricity pricing using a periodic GARCH model with conditional skewness and kurtosis components," Energy Economics, Elsevier, vol. 95(C).
    14. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2023. "Large Time‐Varying Volatility Models for Hourly Electricity Prices," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 85(3), pages 545-573, June.
    15. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.
    16. Rafal Weron & Adam Misiorek, 2006. "Short-term electricity price forecasting with time series models: A review and evaluation," HSC Research Reports HSC/06/01, Hugo Steinhaus Center, Wroclaw University of Technology.
    17. Goodarzi, Shadi & Perera, H. Niles & Bunn, Derek, 2019. "The impact of renewable energy forecast errors on imbalance volumes and electricity spot prices," Energy Policy, Elsevier, vol. 134(C).
    18. Christopher Kath & Florian Ziel, 2018. "The value of forecasts: Quantifying the economic gains of accurate quarter-hourly electricity price forecasts," Papers 1811.08604, arXiv.org.
    19. Weron, Rafał & Zator, Michał, 2015. "A note on using the Hodrick–Prescott filter in electricity markets," Energy Economics, Elsevier, vol. 48(C), pages 1-6.
    20. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
    21. Cao, K.H. & Qi, H.S. & Tsai, C.H. & Woo, C.K. & Zarnikau, J., 2021. "Energy trading efficiency in the US Midcontinent electricity markets," Applied Energy, Elsevier, vol. 302(C).

    More about this item

    Keywords

    Time Series; Forecasting; Electricity Market; Spain; Iran;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    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:eco:journ2:2017-02-33. See general information about how to correct material in RePEc.

    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 bibliographic 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.