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Pairs trading with partial cointegration

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  • Matthew Clegg
  • Christopher Krauss

Abstract

Partial cointegration is a weakening of cointegration that allows for the ‘cointegrating’ residual to contain a random walk and a mean-reverting component. We derive its representation in state space, provide a maximum likelihood-based estimation routine, and a suitable likelihood ratio test. Then, we explore the use of partial cointegration as a means for identifying promising pairs and for generating buy and sell signals. Specifically, we benchmark partial cointegration against several classical pairs trading variants from 1990 until 2015, on a survivor bias free data-set of the S&P 500 constituents. We find annualized returns of more than 12% after transaction costs. These results can only partially be explained by common sources of systematic risk and are well superior to classical distance-based or cointegration-based pairs trading variants on our data-set.

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  • Matthew Clegg & Christopher Krauss, 2018. "Pairs trading with partial cointegration," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 121-138, January.
    • Handle: RePEc:taf:quantf:v:18:y:2018:i:1:p:121-138
    DOI: 10.1080/14697688.2017.1370122
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    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. Hsiu-Chuan Lee & Donald Lien & Her-Jiun Sheu, 2023. "Hedging performance of volatility index futures: a partial cointegration approach," Review of Quantitative Finance and Accounting, Springer, vol. 61(1), pages 265-294, July.
    5. Zhang, Huiming & Qian, Siji & Ma, Zhen, 2024. "An analysis of the market efficiency of the Chinese copper futures based on intertemporal and intermarket arbitrages," International Review of Financial Analysis, Elsevier, vol. 94(C).
    6. Qianqian Mao & Jens-Peter Loy & Thomas Glauben & Yanjun Ren, . "Are futures markets functioning well for agricultural perishables? Evidence from China's apple futures market," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 0.
    7. Qianqian Mao & Jens-Peter Loy & Thomas Glauben & Yanjun Ren, 2023. "Are futures markets functioning well for agricultural perishables? Evidence from China's apple futures market," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 69(12), pages 471-484.
    8. Guang Zhang, 2020. "Pairs Trading with Nonlinear and Non-Gaussian State Space Models," Papers 2005.09794, arXiv.org.
    9. Yen-Sheng Lee, 2022. "Representative Bias and Pairs Trade: Evidence From S&P 500 and Russell 2000 Indexes," SAGE Open, , vol. 12(3), pages 21582440221, August.
    10. Marianna Brunetti & Roberta De Luca, 2023. "Pairs trading in the index options market," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(1), pages 145-173, March.
    11. 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.
    12. Gogolin, Fabian & Kearney, Fearghal & Lucey, Brian M. & Peat, Maurice & Vigne, Samuel A., 2018. "Uncovering long term relationships between oil prices and the economy: A time-varying cointegration analysis," Energy Economics, Elsevier, vol. 76(C), pages 584-593.
    13. Jeff Stephenson & Bruce Vanstone & Tobias Hahn, 2021. "A Unifying Model for Statistical Arbitrage: Model Assumptions and Empirical Failure," Computational Economics, Springer;Society for Computational Economics, vol. 58(4), pages 943-964, December.
    14. Guo, Minjia & Liu, Jianhe & Luo, Ziping & Han, Xiao, 2024. "Deep reinforcement learning for pairs trading: Evidence from China black series futures," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 981-993.
    15. Teresa Vollmer & Helmut Herwartz & Stephan von Cramon-Taubadel, 2020. "Measuring price discovery in the European wheat market using the partial cointegration approach," European Review of Agricultural Economics, Oxford University Press and the European Agricultural and Applied Economics Publications Foundation, vol. 47(3), pages 1173-1200.
    16. 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.
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    18. 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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