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Do we Experience Day-of-the-week Effects in Returns and Volatility of Cryptocurrency?

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  • Yaya, OlaOluwa S
  • Ogbonna, Ephraim A

Abstract

This present paper investigates day-of-the-week effect in some notable cryptocurrency in terms of pricing and market capitalizations. We applied fractional integration regression approach with dummies. We found non-significance of day-of-the-week effect in returns, while there is possible evidence of Monday and Friday effects in volatility of Bitcoin only. Non-significance of day-of-the-week effect in returns of Bitcoin and some other cryptocurrencies further support market efficiency of these markets.

Suggested Citation

  • Yaya, OlaOluwa S & Ogbonna, Ephraim A, 2019. "Do we Experience Day-of-the-week Effects in Returns and Volatility of Cryptocurrency?," MPRA Paper 91429, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:91429
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    References listed on IDEAS

    as
    1. S. Raja Sethu Durai & Sunil Paul, 2018. "Calendar Anomaly and the Degree of Market Inefficiency of Bitcoin," Working Papers 2018-168, Madras School of Economics,Chennai,India.
    2. Caporale, Guglielmo Maria & Plastun, Alex, 2019. "The day of the week effect in the cryptocurrency market," Finance Research Letters, Elsevier, vol. 31(C).
    3. Yutaka Kurihara & Akio Fukushima, 2017. "The Market Efficiency of Bitcoin: A Weekly Anomaly Perspective," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 7(3), pages 1-4.
    4. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    Cited by:

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    2. Chen-Han Liu, 2024. "Exploring Calendar Effects in Bitcoin Returns: An Analysis of Market Efficiency," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 14(4), pages 1-3.
    3. Afees A. Salisu & Ahamuefula E. Ogbonna & Tirimisiyu F. Oloko & Idris A. Adediran, 2021. "A New Index for Measuring Uncertainty Due to the COVID-19 Pandemic," Sustainability, MDPI, vol. 13(6), pages 1-18, March.
    4. Ahamuefula E. Ogbonna & Olusanya E. Olubusoye, 2021. "Tail Risks and Stock Return Predictability - Evidence From Asia-Pacific," Asian Economics Letters, Asia-Pacific Applied Economics Association, vol. 2(3), pages 1-6.
    5. Raifu, Isiaka Akande & Ogbonna, Ahamuefula E, 2021. "Safe-haven Effectiveness of Cryptocurrency: Evidence from Stock Markets of COVID-19 worst-hit African Countries," MPRA Paper 113139, University Library of Munich, Germany.
    6. Olubusoye, Olusanya E & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula, 2021. "An Information-Based Index of Uncertainty and the predictability of Energy Prices," MPRA Paper 109839, University Library of Munich, Germany.
    7. Nuray Tosunoğlu & Hilal Abacı & Gizem Ateş & Neslihan Saygılı Akkaya, 2023. "Artificial neural network analysis of the day of the week anomaly in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-24, December.
    8. Parul Bhatia & Lipika Jain, 2025. "Lawful Sequence of Events and Cryptocurrency Anomalies: An Empirical Investigation," FIIB Business Review, , vol. 14(1), pages 71-88, January.

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    More about this item

    Keywords

    Bitcoin; Day-of-the-week Effect; Cryptocurrency; Market efficiency;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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