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Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

Author

Listed:
  • Natália Costa

    (Instituto Politécnico de Portalegre, 7300-100 Portalegre, Portugal)

  • César Silva

    (Instituto Politécnico de Portalegre, 7300-100 Portalegre, Portugal)

  • Paulo Ferreira

    (Instituto Politécnico de Portalegre, 7300-100 Portalegre, Portugal
    VALORIZA—Research Center for Endogenous Resource Valorization, 7300-555 Portalegre, Portugal
    CEFAGE-UE, IIFA, Universidade de Évora, Largo dos Colegiais 2, 7000 Évora, Portugal)

Abstract

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

Suggested Citation

  • Natália Costa & César Silva & Paulo Ferreira, 2019. "Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies," IJFS, MDPI, vol. 7(3), pages 1-12, September.
    • Handle: RePEc:gam:jijfss:v:7:y:2019:i:3:p:51-:d:267455
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    References listed on IDEAS

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    3. Santos, E.C.O. & Guedes, E.F. & Zebende, G.F. & da Silva Filho, A.M., 2022. "Autocorrelation of wind speed: A sliding window approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
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