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A Stochastic Latent Moment Model for Electricity Price Formation

Citations

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Cited by:

  1. Fabrizio Durante & Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2022. "A Multivariate Dependence Analysis for Electricity Prices, Demand and Renewable Energy Sources," Papers 2201.01132, arXiv.org.
  2. Hasan, Mudassar & Arif, Muhammad & Naeem, Muhammad Abubakr & Ngo, Quang-Thanh & Taghizadeh–Hesary, Farhad, 2021. "Time-frequency connectedness between Asian electricity sectors," Economic Analysis and Policy, Elsevier, vol. 69(C), pages 208-224.
  3. Derek W. Bunn & Angelica Gianfreda & Stefan Kermer, 2018. "A Trading-Based Evaluation of Density Forecasts in a Real-Time Electricity Market," Energies, MDPI, vol. 11(10), pages 1-13, October.
  4. Arthur Thomas & Olivier Massol & Benoît Sévi, 2019. "How are day-ahead prices informative for predicting the next day’s consumption of natural gas?," Post-Print hal-04319396, HAL.
  5. Gianfreda, Angelica & Ravazzolo, Francesco & Rossini, Luca, 2020. "Comparing the forecasting performances of linear models for electricity prices with high RES penetration," International Journal of Forecasting, Elsevier, vol. 36(3), pages 974-986.
  6. Hinderks, W.J. & Wagner, A., 2020. "Factor models in the German electricity market: Stylized facts, seasonality, and calibration," Energy Economics, Elsevier, vol. 85(C).
  7. Meng, Zhu & Zhu, Ning & Zhang, Guowei & Yang, Yuance & Liu, Zhaocai & Ke, Ginger Y., 2024. "Data-driven drone pre-positioning for traffic accident rapid assessment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 183(C).
  8. Sapio, Alessandro & Spagnolo, Nicola, 2020. "The effect of a new power cable on energy prices volatility spillovers," Energy Policy, Elsevier, vol. 144(C).
  9. 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.
  10. Hinderks, W.J. & Wagner, A., 2019. "Pricing German Energiewende products: Intraday cap/floor futures," Energy Economics, Elsevier, vol. 81(C), pages 287-296.
  11. Christos Floros & Emilios Galariotis & Konstantinos Gkillas & Efstathios Magerakis & Constantin Zopounidis, 2024. "Time-varying firm cash holding and economic policy uncertainty nexus: a quantile regression approach," Annals of Operations Research, Springer, vol. 341(2), pages 859-895, October.
  12. Maciejowska, Katarzyna, 2020. "Assessing the impact of renewable energy sources on the electricity price level and variability – A quantile regression approach," Energy Economics, Elsevier, vol. 85(C).
  13. Sapio, Alessandro, 2019. "Greener, more integrated, and less volatile? A quantile regression analysis of Italian wholesale electricity prices," Energy Policy, Elsevier, vol. 126(C), pages 452-469.
  14. Micha{l} Narajewski, 2022. "Probabilistic forecasting of German electricity imbalance prices," Papers 2205.11439, arXiv.org.
  15. Daan Hulshof & Machiel Mulder, 2022. "Design of Renewable Support Schemes and Windfall Profits: A Monte Carlo Analysis for the Netherlands," The Energy Journal, , vol. 43(5), pages 181-204, September.
  16. Fernandes, Mário Correia & Dias, José Carlos & Nunes, João Pedro Vidal, 2021. "Modeling energy prices under energy transition: A novel stochastic-copula approach," Economic Modelling, Elsevier, vol. 105(C).
  17. Taylor, James W. & Taylor, Kathryn S., 2023. "Combining probabilistic forecasts of COVID-19 mortality in the United States," European Journal of Operational Research, Elsevier, vol. 304(1), pages 25-41.
  18. Michał Narajewski, 2022. "Probabilistic Forecasting of German Electricity Imbalance Prices," Energies, MDPI, vol. 15(14), pages 1-17, July.
  19. Angelica Gianfreda & Francesco Ravazzolo & Luca Rossini, 2020. "Large Time-Varying Volatility Models for Electricity Prices," Working Papers No 05/2020, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
  20. Ekaterina Abramova & Derek Bunn, 2021. "Optimal Daily Trading of Battery Operations Using Arbitrage Spreads," Energies, MDPI, vol. 14(16), pages 1-23, August.
  21. Johannes Mauritzen & Genaro Sucarrat, 2022. "Increasing or Diversifying Risk? Tail Correlations, Transmission Flows and Prices across Wind Power Areas," The Energy Journal, , vol. 43(3), pages 105-131, May.
  22. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
  23. Han, Lin & Kordzakhia, Nino & Trück, Stefan, 2020. "Volatility spillovers in Australian electricity markets," Energy Economics, Elsevier, vol. 90(C).
  24. Nadja Klein & Michael Stanley Smith & David J. Nott, 2020. "Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices," Papers 2010.01844, arXiv.org, revised May 2021.
  25. 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.
  26. Koolen, Derck & Huisman, Ronald & Ketter, Wolfgang, 2022. "Decision strategies in sequential power markets with renewable energy," Energy Policy, Elsevier, vol. 167(C).
  27. Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
  28. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Papers 2002.10566, arXiv.org.
  29. Nadja Klein & Michael Stanley Smith & David J. Nott, 2023. "Deep distributional time series models and the probabilistic forecasting of intraday electricity prices," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 38(4), pages 493-511, June.
  30. Katarzyna Maciejowska & Weronika Nitka & Tomasz Weron, 2019. "Day-Ahead vs. Intraday—Forecasting the Price Spread to Maximize Economic Benefits," Energies, MDPI, vol. 12(4), pages 1-15, February.
  31. Ekaterina Abramova & Derek Bunn, 2020. "Forecasting the Intra-Day Spread Densities of Electricity Prices," Energies, MDPI, vol. 13(3), pages 1-31, February.
  32. Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
  33. Nikodinoska, Dragana & Käso, Mathias & Müsgens, Felix, 2022. "Solar and wind power generation forecasts using elastic net in time-varying forecast combinations," Applied Energy, Elsevier, vol. 306(PA).
  34. Huisman, Ronald & Koolen, Derck & Stet, Cristian, 2021. "Pricing forward contracts in power markets with variable renewable energy sources," Renewable Energy, Elsevier, vol. 180(C), pages 1260-1265.
  35. Ekaterina Abramova & Derek Bunn, 2019. "Estimating Dynamic Conditional Spread Densities to Optimise Daily Storage Trading of Electricity," Papers 1903.06668, arXiv.org.
  36. Dong, Zuo & Wang, Xianjia & Zhu, Runzhou & Dong, Xuan & Ai, Xueshan, 2022. "Improving the accuracy of wind speed statistical analysis and wind energy utilization in the Ningxia Autonomous Region, China," Applied Energy, Elsevier, vol. 320(C).
  37. Taylor, James W., 2020. "A strategic predictive distribution for tests of probabilistic calibration," International Journal of Forecasting, Elsevier, vol. 36(4), pages 1380-1388.
  38. Jiang, Zhong-Zhong & He, Na & Huang, Song, 2021. "Government penalty provision and contracting with asymmetric quality information in a bioenergy supply chain," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 154(C).
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