Pairs trading in cryptocurrency market: A long-short story

  • Received May 2, 2021;
    Accepted July 28, 2021;
    Published August 16, 2021
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/imfi.18(3).2021.12
  • Article Info
    Volume 18 2021, Issue #3, pp. 127-141
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Pairs trading that is built on ’Relative-Value Arbitrage Rule’ is a popular short-term speculation strategy enabling traders to make profits from temporary mispricing of close substitutes. This paper aims at investigating the profit potentials of pairs trading in a new finance area – on cryptocurrencies market. The empirical design builds upon four well-known approaches to implement pairs trading, namely: correlation analysis, distance approach, stochastic return differential approach, and cointegration analysis, that use monthly closing prices of leading cryptocoins over the period January 1, 2018, – December 31, 2019. Additionally, the paper executes a simulation exercise that compares long-short strategy with long-only portfolio strategy in terms of payoffs and risks. The study finds an inverse relationship between the correlation coefficient and distance between different pairs of cryptocurrencies, which is a prerequisite to determine the potentially market-neutral profits through pairs trading. In addition, pairs trading simulations produce quite substantive evidence on the continuing profitability of pairs trading. In other words, long-short portfolio strategies, producing positive cumulative returns in most subsample periods, consistently outperform conservative long-only portfolio strategies in the cryptocurrency market. The profitability of pairs trading thus adds empirical challenge to the market efficiency of the cryptocurrency market. However, other aspects like spectral correlations and implied volatility might also be significant in determining the profit potentials of pairs trading.

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    • Figure 1. Cointegration residuals in Panel A
    • Figure 2. Cointegration residuals in Panel B
    • Figure 3. Cointegration residuals in Panel C
    • Figure 4. Cointegration residuals in Panel D
    • Table 1. Summary statistics of cryptocurrencies
    • Table 2. Correlation and distance
    • Table 3. Augmented Dickey-Fuller (ADF) tests on cryptocurrencies prices
    • Table 4. Engle-Granger cointegration analysis of cryptocurrencies
    • Table 5. Augmented Dickey-Fuller (ADF) tests on residual series
    • Table 6. Profit payoffs from pairs trading in simulated exercises
    • Conceptualization
      Saji Thazhungal Govindan Nair
    • Data curation
      Saji Thazhungal Govindan Nair
    • Formal Analysis
      Saji Thazhungal Govindan Nair
    • Methodology
      Saji Thazhungal Govindan Nair
    • Investigation
      Saji Thazhungal Govindan Nair
    • Funding acquisition
      Saji Thazhungal Govindan Nair
    • Project administration
      Saji Thazhungal Govindan Nair
    • Resources
      Saji Thazhungal Govindan Nair
    • Software
      Saji Thazhungal Govindan Nair
    • Supervision
      Saji Thazhungal Govindan Nair
    • Validation
      Saji Thazhungal Govindan Nair
    • Visualization
      Saji Thazhungal Govindan Nair
    • Writing – original draft
      Saji Thazhungal Govindan Nair
    • Writing – review & editing
      Saji Thazhungal Govindan Nair