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Anti-Persistent Values of the Hurst Exponent Anticipate Mean Reversion in Pairs Trading: The Cryptocurrencies Market as a Case Study

Author

Listed:
  • Mar Grande

    (Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Av Puerta de Hierro 2, 28040 Madrid, Spain
    AGrowingData, 04001 Almería, Spain)

  • Florentino Borondo

    (Departamento de Química, Universidad Autónoma de Madrid, Cantoblanco, 28049 Madrid, Spain)

  • Juan Carlos Losada

    (Grupo de Sistemas Complejos, Universidad Politécnica de Madrid, Av Puerta de Hierro 2, 28040 Madrid, Spain)

  • Javier Borondo

    (AGrowingData, 04001 Almería, Spain
    ICAI Engineering School, Universidad Pontificia de Comillas, Alberto Aguilera 23, 28015 Madrid, Spain)

Abstract

Pairs trading is a short-term speculation trading strategy based on matching a long position with a short position in two assets in the hope that their prices will return to their historical equilibrium. In this paper, we focus on identifying opportunities where mean reversion will happen quickly, as the commission costs associated with keeping the positions open for an extended period of time can eliminate excess returns. To this end, we propose the use of the local Hurst exponent as a signal to open trades in the cryptocurrencies market. We conduct a natural experiment to show that the spread of pairs with anti-persistent values of Hurst revert to their mean significantly faster. Next, we verify that this effect is universal across pairs with different levels of co-movement. Finally, we back-test several pairs trading strategies that include H < 0.5 as an indicator and check that all of them result in profits. Hence, we conclude that the Hurst exponent represents a meaningful indicator to detect pairs trading opportunities in the cryptocurrencies market.

Suggested Citation

  • Mar Grande & Florentino Borondo & Juan Carlos Losada & Javier Borondo, 2024. "Anti-Persistent Values of the Hurst Exponent Anticipate Mean Reversion in Pairs Trading: The Cryptocurrencies Market as a Case Study," Mathematics, MDPI, vol. 12(18), pages 1-14, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2911-:d:1480990
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    References listed on IDEAS

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    1. Broussard, John Paul & Vaihekoski, Mika, 2012. "Profitability of pairs trading strategy in an illiquid market with multiple share classes," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(5), pages 1188-1201.
    2. Barkoulas, John T. & Baum, Christopher F., 1996. "Long-term dependence in stock returns," Economics Letters, Elsevier, vol. 53(3), pages 253-259, December.
    3. Fama, Eugene F, 1991. "Efficient Capital Markets: II," Journal of Finance, American Finance Association, vol. 46(5), pages 1575-1617, December.
    4. Engle, Robert & Granger, Clive, 2015. "Co-integration and error correction: Representation, estimation, and testing," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 39(3), pages 106-135.
    5. Lillo Fabrizio & Farmer J. Doyne, 2004. "The Long Memory of the Efficient Market," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 8(3), pages 1-35, September.
    6. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    7. Ramos-Requena, J.P. & Trinidad-Segovia, J.E. & Sánchez-Granero, M.A., 2017. "Introducing Hurst exponent in pair trading," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 488(C), pages 39-45.
    8. Evan Gatev & William N. Goetzmann & K. Geert Rouwenhorst, 2006. "Pairs Trading: Performance of a Relative-Value Arbitrage Rule," The Review of Financial Studies, Society for Financial Studies, vol. 19(3), pages 797-827.
    9. José Pedro Ramos-Requena & Juan Evangelista Trinidad-Segovia & Miguel Ángel Sánchez-Granero, 2020. "Some Notes on the Formation of a Pair in Pairs Trading," Mathematics, MDPI, vol. 8(3), pages 1-17, March.
    10. Huck, Nicolas, 2009. "Pairs selection and outranking: An application to the S&P 100 index," European Journal of Operational Research, Elsevier, vol. 196(2), pages 819-825, July.
    11. Binh Do & Robert Faff, 2010. "Does Simple Pairs Trading Still Work?," Financial Analysts Journal, Taylor & Francis Journals, vol. 66(4), pages 83-95, July.
    12. Bui, Quynh & Ślepaczuk, Robert, 2022. "Applying Hurst Exponent in pair trading strategies on Nasdaq 100 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(C).
    13. Huck, Nicolas, 2010. "Pairs trading and outranking: The multi-step-ahead forecasting case," European Journal of Operational Research, Elsevier, vol. 207(3), pages 1702-1716, December.
    14. Saadet Kasman & Evrim Turgutlu & A. Duygu Ayhan, 2009. "Long memory in stock returns: evidence from the major emerging Central European stock markets," Applied Economics Letters, Taylor & Francis Journals, vol. 16(17), pages 1763-1768.
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