Long memory and structural breaks of cryptocurrencies trading volume

Mohamed Shaker Ahmed () and Elie Bouri
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Mohamed Shaker Ahmed: Cairo University
Elie Bouri: Lebanese American University

Eurasian Economic Review, 2023, vol. 13, issue 3, No 4, 469-497

Abstract: 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.

Keywords: Trading volume; Cryptocurrencies; Stablecoins; Spurious long memory; Long-range dependence; Structural breaks; Out of sample forecasting (search for similar items in EconPapers)
JEL-codes: C43 C54 C58 D80 G01 G41 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s40822-023-00238-8

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