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Constructing cointegrated cryptocurrency portfolios for statistical arbitrage

Author

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  • Tim Leung
  • Hung Nguyen

Abstract

Purpose - This paper aims to present a methodology for constructing cointegrated portfolios consisting of different cryptocurrencies and examines the performance of a number of trading strategies for the cryptocurrency portfolios. Design/methodology/approach - The authors apply a series of statistical methods, including the Johansen test and Engle–Granger test, to derive a linear combination of cryptocurrencies that form a mean-reverting portfolio. Trading systems are designed and different trading strategies with stop-loss constraints are tested and compared according to a set of performance metrics. Findings - The paper finds cointegrated portfolios involving four cryptocurrencies: Bitcoin (BTC), Ethereum (ETH), Bitcoin Cash (BCH) and Litecoin (LTC), and the corresponding trading strategies are shown to be profitable under different configurations. Originality/value - The main contributions of the study are the use of multiple altcoins in addition to bitcoin to construct a cointegrated portfolio, and the detailed comparison of the performance of different trading strategies with and without stop-loss constraints.

Suggested Citation

  • Tim Leung & Hung Nguyen, 2019. "Constructing cointegrated cryptocurrency portfolios for statistical arbitrage," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 36(3), pages 581-599, September.
    • Handle: RePEc:eme:sefpps:sef-08-2018-0264
    DOI: 10.1108/SEF-08-2018-0264
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    Citations

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    Cited by:

    1. Georg Keilbar & Yanfen Zhang, 2021. "On cointegration and cryptocurrency dynamics," Digital Finance, Springer, vol. 3(1), pages 1-23, March.
    2. Tim Leung & Theodore Zhao, 2022. "Adaptive complementary ensemble EMD and energy-frequency spectra of cryptocurrency prices," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-23, March.
    3. Gianna Figá-Talamanca & Sergio Focardi & Marco Patacca, 2021. "Common dynamic factors for cryptocurrencies and multiple pair-trading statistical arbitrages," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 863-882, December.
    4. Burcu Kapar & Jose Olmo, 2021. "Analysis of Bitcoin prices using market and sentiment variables," The World Economy, Wiley Blackwell, vol. 44(1), pages 45-63, January.
    5. 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.
    6. Ahmet Faruk Aysan & Asad Ul Islam Khan & Humeyra Topuz, 2021. "Bitcoin and Altcoins Price Dependency: Resilience and Portfolio Allocation in COVID-19 Outbreak," Risks, MDPI, vol. 9(4), pages 1-13, April.
    7. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    8. Suardi, Sandy & Rasel, Atiqur Rahman & Liu, Bin, 2022. "On the predictive power of tweet sentiments and attention on bitcoin," International Review of Economics & Finance, Elsevier, vol. 79(C), pages 289-301.

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