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Pairs trading based on statistical variability of the spread process

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  • Timofei Bogomolov

Abstract

This research proposes a new non-parametric approach to pairs trading based on renko and kagi constructions which originated from Japanese charting indicators and were introduced to academic studies by Pastukhov. The method exploits statistical information about the variability of the tradable process. The approach does not find a long-run mean of the process and trade towards it like other methods of pairs trading. The only assumption we need is that the statistical properties of the spread process volatility remain reasonably constant. The theoretical profitability of the method has been demonstrated for the Ornstein--Uhlenbeck process. Tests on the daily market data of American and Australian stock exchanges show statistically significant average excess returns ranging from 1.4 to 3.6% per month and annualized Sharpe ratio from 1.5 to 3.4.

Suggested Citation

  • Timofei Bogomolov, 2013. "Pairs trading based on statistical variability of the spread process," Quantitative Finance, Taylor & Francis Journals, vol. 13(9), pages 1411-1430, September.
  • Handle: RePEc:taf:quantf:v:13:y:2013:i:9:p:1411-1430
    DOI: 10.1080/14697688.2012.748934
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    References listed on IDEAS

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    1. Alex Novikov & Nino Kordzakhia, 2007. "Martingales and First Passage Times of AR(1) Sequences," Research Paper Series 205, Quantitative Finance Research Centre, University of Technology, Sydney.
    2. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
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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. 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.
    4. 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.
    5. 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.
    6. Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    7. Johannes Stübinger & 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.
    8. Ahmet Göncü & Erdinc Akyildirim, 2016. "A stochastic model for commodity pairs trading," Quantitative Finance, Taylor & Francis Journals, vol. 16(12), pages 1843-1857, December.
    9. Zhe Huang & Franck Martin, 2017. "Optimal pairs trading strategies in a cointegration framework," Economics Working Paper Archive (University of Rennes 1 & University of Caen) 2017-08, Center for Research in Economics and Management (CREM), University of Rennes 1, University of Caen and CNRS.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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 Dec 2019.

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