Risk factors in the formulation of day-ahead electricity prices: Evidence from the Spanish case
Author
Abstract
Suggested Citation
DOI: 10.1016/j.eneco.2024.108102
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- Xu Han & Mehmet Caner, 2017. "Determining the number of factors with potentially strong within-block correlations in error terms," Econometric Reviews, Taylor & Francis Journals, vol. 36(6-9), pages 946-969, October.
- Jushan Bai & Serena Ng, 2002.
"Determining the Number of Factors in Approximate Factor Models,"
Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Econometric Society World Congress 2000 Contributed Papers 1504, Econometric Society.
- Jushan Bai & Serena Ng, 2000. "Determining the Number of Factors in Approximate Factor Models," Boston College Working Papers in Economics 440, Boston College Department of Economics.
- Mount, Timothy D. & Ning, Yumei & Cai, Xiaobin, 2006. "Predicting price spikes in electricity markets using a regime-switching model with time-varying parameters," Energy Economics, Elsevier, vol. 28(1), pages 62-80, January.
- 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.
- Yunus Emre Ergemen & Niels Haldrup & Carlos Vladimir Rodríguez-Caballero, 2015. "Common long-range dependence in a panel of hourly Nord Pool electricity prices and loads," CREATES Research Papers 2015-58, Department of Economics and Business Economics, Aarhus University.
- Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
- Fanone, Enzo & Gamba, Andrea & Prokopczuk, Marcel, 2013. "The case of negative day-ahead electricity prices," Energy Economics, Elsevier, vol. 35(C), pages 22-34.
- Thomaidis, Nikolaos S. & Christodoulou, Theodoros & Santos-Alamillos, Francisco J., 2023. "Handling the risk dimensions of wind energy generation," Applied Energy, Elsevier, vol. 339(C).
- Nikolaos S. Thomaidis & Gordon H. Dash & Nina Kajiji, 2019. "Common Unobserved Determinants of Intraday Electricity Prices," The Energy Journal, , vol. 40(1_suppl), pages 211-232, June.
- Pirrong, Craig & Jermakyan, Martin, 2008. "The price of power: The valuation of power and weather derivatives," Journal of Banking & Finance, Elsevier, vol. 32(12), pages 2520-2529, December.
- Janczura, Joanna & Weron, Rafal, 2010.
"An empirical comparison of alternate regime-switching models for electricity spot prices,"
Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
- Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models or electricity spot prices," MPRA Paper 20546, University Library of Munich, Germany.
- Michail I. Seitaridis & Nikolaos S. Thomaidis & Pandelis N. Biskas, 2021. "Fundamental Responsiveness in European Electricity Prices," Energies, MDPI, vol. 14(22), pages 1-14, November.
- Huisman, Ronald, 2008. "The influence of temperature on spike probability in day-ahead power prices," Energy Economics, Elsevier, vol. 30(5), pages 2697-2704, September.
- Nikolaos S. Thomaidis & Gordon H. Dash & Nina Kajiji, 2019. "Common Unobserved Determinants of Intraday Electricity Prices," The Energy Journal, International Association for Energy Economics, vol. 0(The New E).
- M. T. Barlow, 2002. "A Diffusion Model For Electricity Prices," Mathematical Finance, Wiley Blackwell, vol. 12(4), pages 287-298, October.
- Ergemen, Yunus Emre & Rodríguez-Caballero, C. Vladimir, 2023.
"Estimation of a dynamic multi-level factor model with possible long-range dependence,"
International Journal of Forecasting, Elsevier, vol. 39(1), pages 405-430.
- Ergemen, Yunus Emre & Rodríguez Caballero, Carlos Vladimir, 2017. "Estimation of a Dynamic Multilevel Factor Model with possible long-range dependence," DES - Working Papers. Statistics and Econometrics. WS 24614, Universidad Carlos III de Madrid. Departamento de EstadÃstica.
- Stock, J.H. & Watson, M.W., 2016. "Dynamic Factor Models, Factor-Augmented Vector Autoregressions, and Structural Vector Autoregressions in Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 415-525, Elsevier.
- Alessi, Lucia & Barigozzi, Matteo & Capasso, Marco, 2010. "Improved penalization for determining the number of factors in approximate factor models," Statistics & Probability Letters, Elsevier, vol. 80(23-24), pages 1806-1813, December.
- Karakatsani, Nektaria V. & Bunn, Derek W., 2008. "Intra-day and regime-switching dynamics in electricity price formation," Energy Economics, Elsevier, vol. 30(4), pages 1776-1797, July.
- Jushan Bai & Peng Wang, 2015. "Identification and Bayesian Estimation of Dynamic Factor Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(2), pages 221-240, April.
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.- 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.
- Rafal Weron, 2014. "Electricity price forecasting: A review of the state-of-the-art with a look into the future," HSC Research Reports HSC/14/07, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
- Janczura, Joanna & Trück, Stefan & Weron, Rafał & Wolff, Rodney C., 2013.
"Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling,"
Energy Economics, Elsevier, vol. 38(C), pages 96-110.
- Janczura, Joanna & Trueck, Stefan & Weron, Rafal & Wolff, Rodney, 2012. "Identifying spikes and seasonal components in electricity spot price data: A guide to robust modeling," MPRA Paper 39277, University Library of Munich, Germany.
- Joanna Janczura, 2012. "Pricing electricity derivatives within a Markov regime-switching model," Papers 1203.5442, arXiv.org.
- Joanna Janczura & Rafal Weron, 2012. "Inference for Markov-regime switching models of electricity spot prices," HSC Research Reports HSC/12/01, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
- Janczura, Joanna & Weron, Rafal, 2010.
"An empirical comparison of alternate regime-switching models for electricity spot prices,"
Energy Economics, Elsevier, vol. 32(5), pages 1059-1073, September.
- Janczura, Joanna & Weron, Rafal, 2010. "An empirical comparison of alternate regime-switching models or electricity spot prices," MPRA Paper 20546, University Library of Munich, Germany.
- Chi-Keung Woo, Ira Horowitz, Brian Horii, Ren Orans, and Jay Zarnikau, 2012.
"Blowing in the Wind: Vanishing Payoffs of a Tolling Agreement for Natural-gas-fired Generation of Electricity in Texas,"
The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
- Chi-Keung Woo & Ira Horowitz & Brian Horii & Ren Orans & Jay Zarnikau, 2011. "Blowing in the Wind: Vanishing Payoffs of a Tolling Agreement for Natural-gas-fired Generation of Electricity in Texas," The Energy Journal, , vol. 33(1), pages 207-230, January.
- Andreas Gerster, 2016. "Negative price spikes at power markets: the role of energy policy," Journal of Regulatory Economics, Springer, vol. 50(3), pages 271-289, December.
- Joanna Janczura & Rafał Weron, 2012.
"Efficient estimation of Markov regime-switching models: An application to electricity spot prices,"
AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 96(3), pages 385-407, July.
- Weron, Rafal & Janczura, Joanna, 2010. "Efficient estimation of Markov regime-switching models: An application to electricity wholesale market prices," MPRA Paper 26628, University Library of Munich, Germany.
- Joanna Janczura & Rafal Weron, 2011. "Efficient estimation of Markov regime-switching models: An application to electricity spot prices," HSC Research Reports HSC/11/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
- 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.
- Janina Ketterer, 2012. "The Impact of Wind Power Generation on the Electricity Price in Germany," ifo Working Paper Series 143, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
- Woo, C.K. & Sreedharan, P. & Hargreaves, J. & Kahrl, F. & Wang, J. & Horowitz, I., 2014. "A review of electricity product differentiation," Applied Energy, Elsevier, vol. 114(C), pages 262-272.
- Woo, C.K. & Chen, Y. & Olson, A. & Moore, J. & Schlag, N. & Ong, A. & Ho, T., 2017. "Electricity price behavior and carbon trading: New evidence from California," Applied Energy, Elsevier, vol. 204(C), pages 531-543.
- Zarnikau, J. & Tsai, C.H. & Woo, C.K., 2020. "Determinants of the wholesale prices of energy and ancillary services in the U.S. Midcontinent electricity market," Energy, Elsevier, vol. 195(C).
- Chi-Keung Woo & Ira Horowitz & Jay Zarnikau & Jack Moore & Brendan Schneiderman & Tony Ho & Eric Leung, 2016.
"What Moves the Ex Post Variable Profit of Natural-Gas-Fired Generation in California?,"
The Energy Journal, , vol. 37(3), pages 29-57, July.
- Chi-Keung Woo, Ira Horowitz, Jay Zarnikau, Jack Moore, Brendan Schneiderman, Tony Ho, and Eric Leung, 2016. "What Moves the Ex Post Variable Profit of Natural-Gas-Fired Generation in California?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
- Freyaldenhoven, Simon, 2022.
"Factor models with local factors — Determining the number of relevant factors,"
Journal of Econometrics, Elsevier, vol. 229(1), pages 80-102.
- Simon Freyaldenhoven, 2021. "Factor Models with Local Factors—Determining the Number of Relevant Factors," Working Papers 21-15, Federal Reserve Bank of Philadelphia.
- Zarnikau, J. & Woo, C.K. & Zhu, S. & Tsai, C.H., 2019. "Market price behavior of wholesale electricity products: Texas," Energy Policy, Elsevier, vol. 125(C), pages 418-428.
- Gerster, Andreas, 2016. "Negative price spikes at power markets: The role of energy policy," Ruhr Economic Papers 636, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
- Woo, C.K. & Moore, J. & Schneiderman, B. & Ho, T. & Olson, A. & Alagappan, L. & Chawla, K. & Toyama, N. & Zarnikau, J., 2016. "Merit-order effects of renewable energy and price divergence in California’s day-ahead and real-time electricity markets," Energy Policy, Elsevier, vol. 92(C), pages 299-312.
- Joanna Janczura, 2014. "Pricing electricity derivatives within a Markov regime-switching model: a risk premium approach," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 79(1), pages 1-30, February.
- Niu, Shilei & Insley, Margaret, 2016. "An options pricing approach to ramping rate restrictions at hydro power plants," Journal of Economic Dynamics and Control, Elsevier, vol. 63(C), pages 25-52.
- Woo, C.K. & Shiu, A. & Liu, Y. & Luo, X. & Zarnikau, J., 2018. "Consumption effects of an electricity decarbonization policy: Hong Kong," Energy, Elsevier, vol. 144(C), pages 887-902.
More about this item
Keywords
Day-ahead electricity market; Fundamental analysis; Structural dynamic factor models; Multi-level factor models;All these keywords.
JEL classification:
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
- Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
- Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources
Statistics
Access and download statisticsCorrections
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:eneeco:v:142:y:2025:i:c:s0140988324008119. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eneco .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.