Research article

Adaptive copula-based pairs trading with market overlay: An enhanced framework for cryptocurrency markets

  • Published: 11 June 2026
  • JEL Codes: C32, C58, G11, G15, G17

  • In this paper, we developed an adaptive copula-based pairs trading framework for cryptocurrency markets and examined the performance of a market overlay variant. Pair selection was conducted using cointegration techniques based on the Augmented Dickey–Fuller (ADF) and Kapetanios–Shin–Snell (KSS) tests. Dependence between paired assets was then modeled using copulas, enabling flexible and nonlinear joint behavior. From the estimated copula models, we constructed a Copula Mispricing Index (CMI) trading signal, with closed-form conditional distributions derived under Gaussian, Student-t, Clayton, Gumbel, and Frank copulas. Copula family selection was guided by the Akaike Information Criterion (AIC) and further validated through goodness-of-fit diagnostics based on the Rosenblatt transform, employing Kolmogorov–Smirnov and Cramér–von Mises uniformity tests. The proposed framework was evaluated using an extensive backtesting exercise on 26,257 hourly observations of ten Binance USDT perpetual futures contracts, spanning January 2021 to December 2023. Empirical results indicated that, under baseline transaction costs of 0.08% per round trip, market-neutral copula-based strategies exhibited relatively low risk, with maximum drawdowns remaining below 20%. However, these strategies generated negative net returns after accounting for trading costs. In contrast, the Alpha Overlay variant, which relaxed strict market neutrality by incorporating directional exposure, closely tracked the buy-and-hold benchmark over the sample period. Overall, the findings highlight the robustness and limitations of copula-based pairs trading in highly volatile cryptocurrency markets, underscoring the importance of transaction costs and strategy design in determining net performance.

    Citation: Edson Pindza, Jules Clement Mba. Adaptive copula-based pairs trading with market overlay: An enhanced framework for cryptocurrency markets[J]. Quantitative Finance and Economics, 2026, 10(2): 378-404. doi: 10.3934/QFE.2026016

    Related Papers:

  • In this paper, we developed an adaptive copula-based pairs trading framework for cryptocurrency markets and examined the performance of a market overlay variant. Pair selection was conducted using cointegration techniques based on the Augmented Dickey–Fuller (ADF) and Kapetanios–Shin–Snell (KSS) tests. Dependence between paired assets was then modeled using copulas, enabling flexible and nonlinear joint behavior. From the estimated copula models, we constructed a Copula Mispricing Index (CMI) trading signal, with closed-form conditional distributions derived under Gaussian, Student-t, Clayton, Gumbel, and Frank copulas. Copula family selection was guided by the Akaike Information Criterion (AIC) and further validated through goodness-of-fit diagnostics based on the Rosenblatt transform, employing Kolmogorov–Smirnov and Cramér–von Mises uniformity tests. The proposed framework was evaluated using an extensive backtesting exercise on 26,257 hourly observations of ten Binance USDT perpetual futures contracts, spanning January 2021 to December 2023. Empirical results indicated that, under baseline transaction costs of 0.08% per round trip, market-neutral copula-based strategies exhibited relatively low risk, with maximum drawdowns remaining below 20%. However, these strategies generated negative net returns after accounting for trading costs. In contrast, the Alpha Overlay variant, which relaxed strict market neutrality by incorporating directional exposure, closely tracked the buy-and-hold benchmark over the sample period. Overall, the findings highlight the robustness and limitations of copula-based pairs trading in highly volatile cryptocurrency markets, underscoring the importance of transaction costs and strategy design in determining net performance.



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    [1] Al-Yahyaee KH, Rehman MU, Mensi W, et al. (2020) Why cryptocurrency markets are inefficient: The impact of liquidity and volatility. North Am J Econ Financ 52: 101168. https://doi.org/10.1016/j.najef.2020.101168 doi: 10.1016/j.najef.2020.101168
    [2] Borri N, Shakhnov K (2020) Regulation spillovers across cryptocurrency markets. Financ Res Lett 36: 101333. https://doi.org/10.1016/j.frl.2019.101333 doi: 10.1016/j.frl.2019.101333
    [3] Cheng T, Lesmana NS, Poreddy SR, et al. (2025) Predictive uncertainty quantification for financial DNN using regular vine copula. In: Proceedings of the 6th ACM International Conference on AI in Finance, 873–881. https://doi.org/10.1145/3768292.3771254
    [4] Clegg M, Krauss C (2018) Pairs trading with partial cointegration. Quant Financ 18: 121–138. https://doi.org/10.1080/14697688.2017.1370122 doi: 10.1080/14697688.2017.1370122
    [5] Do B, Faff R (2010) Does simple pairs trading still work? Financ Anal J 66: 83–95. https://doi.org/10.2469/faj.v66.n4.1 doi: 10.2469/faj.v66.n4.1
    [6] Do B, Faff R (2012) Are pairs trading profits robust to trading costs? J Financ Res 35: 261–287. https://doi.org/10.1111/j.1475-6803.2012.01317.x doi: 10.1111/j.1475-6803.2012.01317.x
    [7] Fakhfekh M, Bejaoui A, Bariviera AF, Jeribi A (2024) Dependence structure between NFT, DeFi and cryptocurrencies in turbulent times: An Archimax copula approach. North Am J Econ Financ 70: 102079. https://doi.org/10.1016/j.najef.2024.102079 doi: 10.1016/j.najef.2024.102079
    [8] Fil M, Kristoufek L (2020) Pairs trading in cryptocurrency markets. IEEE Access 8: 172644–172651. https://doi.org/10.1109/ACCESS.2020.3024619 doi: 10.1109/ACCESS.2020.3024619
    [9] Gatev E, Goetzmann WN, Rouwenhorst KG (2006) Pairs trading: Performance of a relative-value arbitrage rule. Rev Financ Stud 19: 797–827. https://doi.org/10.1093/rfs/hhj020 doi: 10.1093/rfs/hhj020
    [10] Genest C, Rémillard B, Beaudoin D (2009) Goodness-of-fit tests for copulas: A review and a power study. Insur Math Econ 44: 199–213. https://doi.org/10.1016/j.insmatheco.2007.10.005 doi: 10.1016/j.insmatheco.2007.10.005
    [11] Kapetanios G, Shin Y, Snell A (2003) Testing for a unit root in the nonlinear STAR framework. J Econometrics 112: 359–379. https://doi.org/10.1016/S0304-4076(02)00202-6 doi: 10.1016/S0304-4076(02)00202-6
    [12] Krauss C, Stübinger J (2017) Non-linear dependence modeling with bivariate copulas: Statistical arbitrage pairs trading on the S&P 100. Appl Econ 49: 5352–5369. https://doi.org/10.1080/00036846.2017.1305097 doi: 10.1080/00036846.2017.1305097
    [13] Liew RQ, Yuan W (2013) Pairs trading: A copula approach. J Deriv Hedge Funds 19: 12–30. https://doi.org/10.1057/jdhf.2013.1 doi: 10.1057/jdhf.2013.1
    [14] Lintilhac PS, Tourin A (2017) Model-based pairs trading in the bitcoin markets. Quant Financ 17: 703–716. https://doi.org/10.1080/14697688.2016.1231928 doi: 10.1080/14697688.2016.1231928
    [15] MacKinnon JG (1991) Critical values for cointegration tests. In: Long-Run Economic Relationships, Oxford University Press, Oxford, 267–276. DOI not available
    [16] Queiroz RGS, Kristoufek L, David SA (2024) A combined framework to explore cryptocurrency volatility and dependence using multivariate GARCH and Copula modeling. Physica A 652: 130046. https://doi.org/10.1016/j.physa.2024.130046 doi: 10.1016/j.physa.2024.130046
    [17] Rad H, Low RKY, Faff R (2016) The profitability of pairs trading strategies: Distance, cointegration and copula methods. Quant Financ 16: 1541–1558. https://doi.org/10.1080/14697688.2016.1164337 doi: 10.1080/14697688.2016.1164337
    [18] Stübinger J, Mangold B, Krauss C (2018) Statistical arbitrage with vine copulas. Quant Financ 18: 1831–1849. https://doi.org/10.1080/14697688.2018.1438642 doi: 10.1080/14697688.2018.1438642
    [19] Tadi M, Witzany J (2025) Copula-based trading of cointegrated cryptocurrency pairs. Financial Innovation 11: 40. https://doi.org/10.1186/s40854-024-00702-7 doi: 10.1186/s40854-024-00702-7
    [20] Tenkam HM, Mba JC, Mwambi SM (2022) Optimization and diversification of cryptocurrency portfolios: A composite copula-based approach. Appl Sci 12(13): 6408. https://doi.org/10.3390/app12136408 doi: 10.3390/app12136408
    [21] Vidyamurthy G (2004) Pairs Trading: Quantitative Methods and Analysis. Wiley, Hoboken.
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