IDEAS home Printed from https://ideas.repec.org/p/lde/grupom/074.html
   My bibliography  Save this paper

Sobre Los Fundamentales Del Precio De La Energía Eléctrica: Evidencia Empírica Para Colombia

Author

Listed:
  • Jorge Barrientos Marín

    (Universidad de Antioquia)

  • Mónica Toro Martínez

    (Universidad de Antioquia)

Abstract

in this paper we are interested in investigating the market fundamentals that influences energy prices formation in Colombia and evaluating the impact of the some market variables on the behavior of energy price by estimating the impulse-response function. To this end we estimate VAR specification. In addition, we carried out an exploratory analysis for forecasting the future energy prices in the next 10 years. Our main conclusion is that the set of variables which most affects the evolution of the energy prices is the hydrology and the declared availability. About the forecasting, we found that the energy prices going to increase for the next years with a kind of fall around 2018 just for recovery ahead.

Suggested Citation

  • Jorge Barrientos Marín & Mónica Toro Martínez, 2016. "Sobre Los Fundamentales Del Precio De La Energía Eléctrica: Evidencia Empírica Para Colombia," Grupo Microeconomía Aplicada 74, Universidad de Antioquia, Departamento de Economía.
    • Handle: RePEc:lde:grupom:074
    as

    Download full text from publisher

    File URL: ftp://ftp.drivehq.com/cavasco/grupom/jbarr_1.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Haldrup, Niels & Nielsen, Frank S. & Nielsen, Morten Ørregaard, 2010. "A vector autoregressive model for electricity prices subject to long memory and regime switching," Energy Economics, Elsevier, vol. 32(5), pages 1044-1058, September.
    2. Sergio Botero Botero & Jovan Alfonso Cano Cano, 2008. "Análisis de series de tiempo para la predicción de los precios de la energía en la bolsa de Colombia," Revista Cuadernos de Economia, Universidad Nacional de Colombia, FCE, CID, July.
    3. 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.
    4. Barrientos Marín, Jorge & Rodas, Edwin & Velilla, Esteban & Lopera, Mauricio, 2012. "Modelo para el pronóstico del precio de la energía eléctrica en Colombia," Revista Lecturas de Economía, Universidad de Antioquia, CIE, October.
    5. Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Forecasting electricity prices: The impact of fundamentals and time-varying coefficients," International Journal of Forecasting, Elsevier, vol. 24(4), pages 764-785.
    6. Larsen, Erik R. & Dyner, Isaac & Bedoya V., Leonardo & Franco, Carlos Jaime, 2004. "Lessons from deregulation in Colombia: successes, failures and the way ahead," Energy Policy, Elsevier, vol. 32(15), pages 1767-1780, October.
    7. Huisman, Ronald & Mahieu, Ronald, 2003. "Regime jumps in electricity prices," Energy Economics, Elsevier, vol. 25(5), pages 425-434, September.
    Full references (including those not matched with items on IDEAS)

    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. Jorge Barrientos Marín & Mónica Toro Martínez, 2017. "Análisis de los fundamentales del precio de la energía eléctrica: evidencia empírica para Colombia," Revista de Economía del Caribe 17148, Universidad del Norte.
    2. Godin, Frédéric & Ibrahim, Zinatu, 2021. "An analysis of electricity congestion price patterns in North America," Energy Economics, Elsevier, vol. 102(C).
    3. Jorge Barrientos Marin & Elkin Tabares Orozco & Esteban Velilla, 2018. "Forecasting electricity price in Colombia: A comparison between Neural Network, ARMA process and Hybrid Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 97-106.
    4. Frömmel, Michael & Han, Xing & Kratochvil, Stepan, 2014. "Modeling the daily electricity price volatility with realized measures," Energy Economics, Elsevier, vol. 44(C), pages 492-502.
    5. Martin de Lagarde, Cyril & Lantz, Frédéric, 2018. "How renewable production depresses electricity prices: Evidence from the German market," Energy Policy, Elsevier, vol. 117(C), pages 263-277.
    6. Gianfreda, Angelica & Grossi, Luigi, 2012. "Forecasting Italian electricity zonal prices with exogenous variables," Energy Economics, Elsevier, vol. 34(6), pages 2228-2239.
    7. Florian Ziel & Rick Steinert & Sven Husmann, 2015. "Forecasting day ahead electricity spot prices: The impact of the EXAA to other European electricity markets," Papers 1501.00818, arXiv.org, revised Dec 2015.
    8. 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.
    9. Michel Culot & Valérie Goffin & Steve Lawford & Sébastien de Meten & Yves Smeers, 2013. "Practical stochastic modelling of electricity prices," Post-Print hal-01021603, HAL.
    10. 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.
    11. Weron, Rafal & Misiorek, Adam, 2008. "Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models," International Journal of Forecasting, Elsevier, vol. 24(4), pages 744-763.
    12. Zachmann, Georg, 2013. "A stochastic fuel switching model for electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 5-13.
    13. Smith, Michael Stanley & Shively, Thomas S., 2018. "Econometric modeling of regional electricity spot prices in the Australian market," Energy Economics, Elsevier, vol. 74(C), pages 886-903.
    14. Kyritsis, Evangelos & Andersson, Jonas & Serletis, Apostolos, 2017. "Electricity prices, large-scale renewable integration, and policy implications," Energy Policy, Elsevier, vol. 101(C), pages 550-560.
    15. Eichler, M. & Türk, D., 2013. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Energy Economics, Elsevier, vol. 36(C), pages 614-624.
    16. Ergemen, Yunus Emre & Haldrup, Niels & Rodríguez-Caballero, Carlos Vladimir, 2016. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," Energy Economics, Elsevier, vol. 60(C), pages 79-96.
    17. Girum Dagnachew Abate & Niels Haldrup, 2017. "Space-time modeling of electricity spot prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 5).
    18. Stephen Machin & Olivier Marie & Sunčica Vujić, 2012. "Youth Crime and Education Expansion," German Economic Review, Verein für Socialpolitik, vol. 13(4), pages 366-384, November.
    19. Bordignon, Silvano & Bunn, Derek W. & Lisi, Francesco & Nan, Fany, 2013. "Combining day-ahead forecasts for British electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 88-103.
    20. Eichler, M. & Türk, D.D.T., 2012. "Fitting semiparametric Markov regime-switching models to electricity spot prices," Research Memorandum 035, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).

    More about this item

    Keywords

    Spot market; electricity prices; VAR; forecasting; impulse response function;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D43 - Microeconomics - - Market Structure, Pricing, and Design - - - Oligopoly and Other Forms of Market Imperfection
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:lde:grupom:074. 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: Carlos Andrés Vasco Correa (email available below). General contact details of provider: https://edirc.repec.org/data/deantco.html .

    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.