{"title":"加密货币交易量的长记忆和结构性突破","authors":"Mohamed Shaker Ahmed, Elie Bouri","doi":"10.1007/s40822-023-00238-8","DOIUrl":null,"url":null,"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.","PeriodicalId":45064,"journal":{"name":"Eurasian Economic Review","volume":"1 1","pages":"0"},"PeriodicalIF":2.5000,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Long memory and structural breaks of cryptocurrencies trading volume\",\"authors\":\"Mohamed Shaker Ahmed, Elie Bouri\",\"doi\":\"10.1007/s40822-023-00238-8\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":45064,\"journal\":{\"name\":\"Eurasian Economic Review\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":2.5000,\"publicationDate\":\"2023-10-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Eurasian Economic Review\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1007/s40822-023-00238-8\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ECONOMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Eurasian Economic Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s40822-023-00238-8","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ECONOMICS","Score":null,"Total":0}
Long memory and structural breaks of cryptocurrencies trading volume
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.
期刊介绍：
The mission of Eurasian Economic Review is to publish peer-reviewed empirical research papers that test, extend, or build theory and contribute to practice. All empirical methods - including, but not limited to, qualitative, quantitative, field, laboratory, and any combination of methods - are welcome. Empirical, theoretical and methodological articles from all fields of finance and applied macroeconomics are featured in the journal. Theoretical and/or review articles that integrate existing bodies of research and that provide new insights into the field are highly encouraged. The journal has a broad scope, addressing such issues as: financial systems and regulation, corporate and start-up finance, macro and sustainable finance, finance and innovation, consumer finance, public policies on financial markets within local, regional, national and international contexts, money and banking, and the interface of labor and financial economics. The macroeconomics coverage includes topics from monetary economics, labor economics, international economics and development economics.
Eurasian Economic Review is published quarterly. To be published in Eurasian Economic Review, a manuscript must make strong empirical and/or theoretical contributions and highlight the significance of those contributions to our field. Consequently, preference is given to submissions that test, extend, or build strong theoretical frameworks while empirically examining issues with high importance for theory and practice. Eurasian Economic Review is not tied to any national context. Although it focuses on Europe and Asia, all papers from related fields on any region or country are highly encouraged. Single country studies, cross-country or regional studies can be submitted.
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