Statistical arbitrage trading on the intraday market using the asynchronous advantage actor–critic method
Author
Abstract
Suggested Citation
DOI: 10.1016/j.apenergy.2022.118912
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
- Severin Borenstein & James Bushnell & Christopher R. Knittel & Catherine Wolfram, 2008.
"Inefficiencies And Market Power In Financial Arbitrage: A Study Of California'S Electricity Markets,"
Journal of Industrial Economics, Wiley Blackwell, vol. 56(2), pages 347-378, June.
- Borenstein, Severin & Bushnell, James & Wolfram, Catherine D, 2006. "Inefficiencies and Market Power in Financial Arbitrage: A Study of California’s Electricity Markets," Institute of Transportation Studies, Working Paper Series qt7fp26301, Institute of Transportation Studies, UC Davis.
- Borenstein, Severin & Bushnell, James & Knittel, Chris & Wolfram, Catherine, 2008. "Inefficiencies and Market Power in Financial Arbitrage: A Study of California's Electricity Markets," Staff General Research Papers Archive 13133, Iowa State University, Department of Economics.
- Christopher Knittel & Catherine Wolfram & James Bushnell & Severin Borenstein, 2006. "Inefficiencies and Market Power in Financial Arbitrage: A Study of California?s Electricity Markets," Working Papers 105, University of California, Davis, Department of Economics.
- Qiu, Dawei & Ye, Yujian & Papadaskalopoulos, Dimitrios & Strbac, Goran, 2021. "Scalable coordinated management of peer-to-peer energy trading: A multi-cluster deep reinforcement learning approach," Applied Energy, Elsevier, vol. 292(C).
- Dorokhova, Marina & Martinson, Yann & Ballif, Christophe & Wyrsch, Nicolas, 2021. "Deep reinforcement learning control of electric vehicle charging in the presence of photovoltaic generation," Applied Energy, Elsevier, vol. 301(C).
- René Aïd & P. Gruet & H. Pham, 2016. "An optimal trading problem in intraday electricity markets," Post-Print hal-01609481, HAL.
- Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
- Ruoyang Li & Alva Svoboda & Shmuel Oren, 2015. "Efficiency impact of convergence bidding in the california electricity market," Journal of Regulatory Economics, Springer, vol. 48(3), pages 245-284, December.
- Bertrand, Gilles & Papavasiliou, Anthony, 2020. "Adaptive Trading in Continuous Intraday Electricity Markets for a Storage Unit," LIDAM Reprints CORE 3104, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
- Micha{l} Narajewski & Florian Ziel, 2020. "Ensemble Forecasting for Intraday Electricity Prices: Simulating Trajectories," Papers 2005.01365, arXiv.org, revised Aug 2020.
- Narajewski, Michał & Ziel, Florian, 2020. "Ensemble forecasting for intraday electricity prices: Simulating trajectories," Applied Energy, Elsevier, vol. 279(C).
- Hadsell, Lester, 2007. "The impact of virtual bidding on price volatility in New York's wholesale electricity market," Economics Letters, Elsevier, vol. 95(1), pages 66-72, April.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Yang, Kun & Cheng, Zishu & Li, Mingchen & Wang, Shouyang & Wei, Yunjie, 2024. "Fortify the investment performance of crude oil market by integrating sentiment analysis and an interval-based trading strategy," Applied Energy, Elsevier, vol. 353(PA).
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.- Micha{l} Narajewski & Florian Ziel, 2021. "Optimal bidding in hourly and quarter-hourly electricity price auctions: trading large volumes of power with market impact and transaction costs," Papers 2104.14204, arXiv.org, revised Feb 2022.
- Christopher Kath & Florian Ziel, 2020. "Optimal Order Execution in Intraday Markets: Minimizing Costs in Trade Trajectories," Papers 2009.07892, arXiv.org, revised Oct 2020.
- Narajewski, Michał & Ziel, Florian, 2022. "Optimal bidding in hourly and quarter-hourly electricity price auctions: Trading large volumes of power with market impact and transaction costs," Energy Economics, Elsevier, vol. 110(C).
- Benedikt Finnah, 2022. "Optimal bidding functions for renewable energies in sequential electricity markets," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 44(1), pages 1-27, March.
- Thomas Deschatre & Xavier Warin, 2023. "A Common Shock Model for multidimensional electricity intraday price modelling with application to battery valuation," Papers 2307.16619, arXiv.org.
- Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Papers 2404.03968, arXiv.org.
- Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
- Finnah, Benedikt & Gönsch, Jochen & Ziel, Florian, 2022. "Integrated day-ahead and intraday self-schedule bidding for energy storage systems using approximate dynamic programming," European Journal of Operational Research, Elsevier, vol. 301(2), pages 726-746.
- Li, Wei & Paraschiv, Florentina, 2022. "Modelling the evolution of wind and solar power infeed forecasts," Journal of Commodity Markets, Elsevier, vol. 25(C).
- Hopkins, Caroline A., 2020. "Convergence bids and market manipulation in the California electricity market," Energy Economics, Elsevier, vol. 89(C).
- Simon Hirsch & Florian Ziel, 2022. "Simulation-based Forecasting for Intraday Power Markets: Modelling Fundamental Drivers for Location, Shape and Scale of the Price Distribution," Papers 2211.13002, arXiv.org.
- Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021.
"Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark,"
Applied Energy, Elsevier, vol. 293(C).
- Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafa{l} Weron, 2020. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Papers 2008.08004, arXiv.org, revised Dec 2020.
- Hilger, Hannes & Witthaut, Dirk & Dahmen, Manuel & Rydin Gorjão, Leonardo & Trebbien, Julius & Cramer, Eike, 2024. "Multivariate scenario generation of day-ahead electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 367(C).
- Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022.
"Short-term risk management of electricity retailers under rising shares of decentralized solar generation,"
Energy Economics, Elsevier, vol. 109(C).
- Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2021. "Short-term risk management for electricity retailers under rising shares of decentralized solar generation," Working Paper Series in Production and Energy 57, Karlsruhe Institute of Technology (KIT), Institute for Industrial Production (IIP).
- Homod, Raad Z. & Togun, Hussein & Kadhim Hussein, Ahmed & Noraldeen Al-Mousawi, Fadhel & Yaseen, Zaher Mundher & Al-Kouz, Wael & Abd, Haider J. & Alawi, Omer A. & Goodarzi, Marjan & Hussein, Omar A., 2022. "Dynamics analysis of a novel hybrid deep clustering for unsupervised learning by reinforcement of multi-agent to energy saving in intelligent buildings," Applied Energy, Elsevier, vol. 313(C).
- Rainer Baule & Michael Naumann, 2021. "Volatility and Dispersion of Hourly Electricity Contracts on the German Continuous Intraday Market," Energies, MDPI, vol. 14(22), pages 1-24, November.
- Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
- Nametala, Ciniro Aparecido Leite & Faria, Wandry Rodrigues & Lage, Guilherme Guimarães & Pereira, Benvindo Rodrigues, 2023. "Analysis of hourly price granularity implementation in the Brazilian deregulated electricity contracting environment," Utilities Policy, Elsevier, vol. 81(C).
- Silvia Golia & Luigi Grossi & Matteo Pelagatti, 2022. "Machine Learning Models and Intra-Daily Market Information for the Prediction of Italian Electricity Prices," Forecasting, MDPI, vol. 5(1), pages 1-21, December.
- Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023.
"Distributional neural networks for electricity price forecasting,"
Energy Economics, Elsevier, vol. 125(C).
- Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022. "Distributional neural networks for electricity price forecasting," Papers 2207.02832, arXiv.org, revised Dec 2022.
More about this item
Keywords
Algorithmic trading; Actor–critic; A3C; Behaviour cloning; Deep reinforcement learning; Intraday markets; Non-physical virtual trader; Single intraday coupled market; Statistical arbitrage;All these keywords.
JEL classification:
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:appene:v:314:y:2022:i:c:s0306261922003348. 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/wps/find/journaldescription.cws_home/405891/description#description .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.