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Long memory and structural breaks of cryptocurrencies trading volume

Author

Listed:
  • Mohamed Shaker Ahmed

    (Cairo University
    Rabdan Academy)

  • Elie Bouri

    (Lebanese American University)

Abstract

The paper investigates long memory, structural breaks, and spurious long memory in the daily trading volume of the largest and most active cryptocurrencies and stablecoins, namely, Bitcoin, Ethereum, Tether, USD coin, Binance coin, Binance USD, Ripple, Cardano, Solana, Dogecoin and Bitcoin cash. The overall results show that both long memory and structural breaks are present in the cryptocurrencies trading volume, and the detected long memory property is not driven by structural breaks but rather true and thus not spurious. Given this, we conduct out-of-sample forecasting and indicate that the ARFIMA model, which accounts for long-range dependence, has a superior forecasting performance over the standard ARIMA model for four cryptocurrencies, namely, Binance coin, Ripple, Cardano, and Dogecoin at most forecasting horizons ahead and the shorter forecasting horizon (1-day ahead) for most cryptocurrencies under investigation.

Suggested Citation

  • Mohamed Shaker Ahmed & Elie Bouri, 2023. "Long memory and structural breaks of cryptocurrencies trading volume," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 13(3), pages 469-497, December.
    • Handle: RePEc:spr:eurase:v:13:y:2023:i:3:d:10.1007_s40822-023-00238-8
    DOI: 10.1007/s40822-023-00238-8
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    More about this item

    Keywords

    Trading volume; Cryptocurrencies; Stablecoins; Spurious long memory; Long-range dependence; Structural breaks; Out of sample forecasting;
    All these keywords.

    JEL classification:

    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • C54 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Quantitative Policy Modeling
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D80 - Microeconomics - - Information, Knowledge, and Uncertainty - - - General
    • G01 - Financial Economics - - General - - - Financial Crises
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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