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Pairs trading based on statistical variability of the spread process

Citations

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Cited by:

  1. Endres, Sylvia & Stübinger, Johannes, 2017. "Optimal trading strategies for Lévy-driven Ornstein-Uhlenbeck processes," FAU Discussion Papers in Economics 17/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  2. Krauss, Christopher & Stübinger, Johannes, 2015. "Nonlinear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100," FAU Discussion Papers in Economics 15/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  3. Flori, Andrea & Regoli, Daniele, 2021. "Revealing Pairs-trading opportunities with long short-term memory networks," European Journal of Operational Research, Elsevier, vol. 295(2), pages 772-791.
  4. Xiang, Yun & He, Jiaxuan, 2022. "Pairs trading and asset pricing," Pacific-Basin Finance Journal, Elsevier, vol. 72(C).
  5. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
  6. Ahmet Göncü & Erdinc Akyildirim, 2016. "A stochastic model for commodity pairs trading," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1843-1857, December.
  7. Bo Liu & Lo-Bin Chang & Hélyette Geman, 2017. "Intraday pairs trading strategies on high frequency data: the case of oil companies," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 87-100, January.
  8. Stübinger, Johannes & Endres, Sylvia, 2017. "Pairs trading with a mean-reverting jump-diffusion model on high-frequency data," FAU Discussion Papers in Economics 10/2017, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  9. Ning Fu & Mingu Kang & Joongi Hong & Suntae Kim, 2024. "Enhanced Genetic-Algorithm-Driven Triple Barrier Labeling Method and Machine Learning Approach for Pair Trading Strategy in Cryptocurrency Markets," Mathematics, MDPI, vol. 12(5), pages 1-21, March.
  10. Clegg, Matthew & Krauss, Christopher, 2016. "Pairs trading with partial cointegration," FAU Discussion Papers in Economics 05/2016, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  11. Zura Kakushadze & Juan Andrés Serur, 2018. "151 Trading Strategies," Springer Books, Springer, number 978-3-030-02792-6, March.
  12. Guang Zhang, 2020. "Pairs Trading with Nonlinear and Non-Gaussian State Space Models," Papers 2005.09794, arXiv.org.
  13. Krauss, Christopher & Do, Xuan Anh & Huck, Nicolas, 2017. "Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500," European Journal of Operational Research, Elsevier, vol. 259(2), pages 689-702.
  14. Hossein Rad & Rand Kwong Yew Low & Robert Faff, 2016. "The profitability of pairs trading strategies: distance, cointegration and copula methods," Quantitative Finance, Taylor & Francis Journals, vol. 16(10), pages 1541-1558, October.
  15. Krauss, Christopher & Krüger, Tom & Beerstecher, Daniel, 2015. "The Piotroski F-Score: A fundamental value strategy revisited from an investor's perspective," FAU Discussion Papers in Economics 13/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  16. Johannes St binger & Jens Bredthauer, 2017. "Statistical Arbitrage Pairs Trading with High-frequency Data," International Journal of Economics and Financial Issues, Econjournals, vol. 7(4), pages 650-662.
  17. Psaradellis, Ioannis & Laws, Jason & Pantelous, Athanasios A. & Sermpinis, Georgios, 2023. "Technical analysis, spread trading, and data snooping control," International Journal of Forecasting, Elsevier, vol. 39(1), pages 178-191.
  18. Zhe Huang & Franck Martin, 2017. "Optimal pairs trading strategies in a cointegration framework," Economics Working Paper Archive (University of Rennes & University of Caen) 2017-08, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
  19. Krauss, Christopher, 2015. "Statistical arbitrage pairs trading strategies: Review and outlook," FAU Discussion Papers in Economics 09/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  20. Fischer, Thomas & Krauss, Christopher, 2018. "Deep learning with long short-term memory networks for financial market predictions," European Journal of Operational Research, Elsevier, vol. 270(2), pages 654-669.
  21. Masood Tadi & Jiří Witzany, 2025. "Copula-based trading of cointegrated cryptocurrency Pairs," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-32, December.
  22. Johannes Stübinger & Sylvia Endres, 2018. "Pairs trading with a mean-reverting jump–diffusion model on high-frequency data," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1735-1751, October.
  23. Krauss, Christopher & Beerstecher, Daniel & Krüger, Tom, 2015. "Feasible earnings momentum in the U.S. stock market: An investor's perspective," FAU Discussion Papers in Economics 12/2015, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
  24. Jia Miao & Jason Laws, 2016. "Profitability Of A Simple Pairs Trading Strategy: Recent Evidences From A Global Context," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-18, June.
  25. Vladim'ir Hol'y & Petra Tomanov'a, 2018. "Estimation of Ornstein-Uhlenbeck Process Using Ultra-High-Frequency Data with Application to Intraday Pairs Trading Strategy," Papers 1811.09312, arXiv.org, revised Jul 2022.
  26. Fernando Caneo & Werner Kristjanpoller, 2021. "Improving statistical arbitrage investment strategy: Evidence from Latin American stock markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(3), pages 4424-4440, July.
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