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Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification

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  • Yarovaya, Larisa
  • Zięba, Damian

Abstract

This paper analyses the volume-return relationships across the top 30 most traded cryptocurrencies from February 2018 to July 2019 using high-frequency intraday data. We use a novel approach for the classification of cryptocurrencies with respect to multiple qualitative factors, such as geographical location of headquarters, founder and founder’s origin, platform on which the cryptocurrency is built, and consensus algorithm, among others. We identify significant bidirectional causalities between trading volume and returns at different high-frequency intervals; however, those linkages are weakening with decreasing data frequencies. The findings confirm the leading position of the Bitcoin trading volume in the cryptocurrency price formation. This evidence will help investors to design effective trading strategies in cryptocurrency markets providing useful insights from cryptocurrency categorisation.

Suggested Citation

  • Yarovaya, Larisa & Zięba, Damian, 2022. "Intraday volume-return nexus in cryptocurrency markets: Novel evidence from cryptocurrency classification," Research in International Business and Finance, Elsevier, vol. 60(C).
    • Handle: RePEc:eee:riibaf:v:60:y:2022:i:c:s0275531921002130
    DOI: 10.1016/j.ribaf.2021.101592
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    Cited by:

    1. Chu, Jeffrey & Chan, Stephen & Zhang, Yuanyuan, 2023. "An analysis of the return–volume relationship in decentralised finance (DeFi)," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 236-254.
    2. Katsiampa, Paraskevi & Yarovaya, Larisa & Zięba, Damian, 2022. "High-frequency connectedness between Bitcoin and other top-traded crypto assets during the COVID-19 crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 79(C).
    3. Şoiman, Florentina & Dumas, Jean-Guillaume & Jimenez-Garces, Sonia, 2023. "What drives DeFi market returns?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 85(C).
    4. Naeem, Muhammad Abubakr & Yousaf, Imran & Karim, Sitara & Yarovaya, Larisa & Ali, Shoaib, 2023. "Tail-event driven NETwork dependence in emerging markets," Emerging Markets Review, Elsevier, vol. 55(C).
    5. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Zhao, Yuan & Liu, Nan & Li, Wanpeng, 2022. "Industry herding in crypto assets," International Review of Financial Analysis, Elsevier, vol. 84(C).
    7. Ghosh, Indranil & Alfaro-Cortés, Esteban & Gámez, Matías & García-Rubio, Noelia, 2023. "Prediction and interpretation of daily NFT and DeFi prices dynamics: Inspection through ensemble machine learning & XAI," International Review of Financial Analysis, Elsevier, vol. 87(C).
    8. José Antonio Núñez-Mora & Mario Iván Contreras-Valdez & Roberto Joaquín Santillán-Salgado, 2023. "Risk Premium of Bitcoin and Ethereum during the COVID-19 and Non-COVID-19 Periods: A High-Frequency Approach," Mathematics, MDPI, vol. 11(20), pages 1-20, October.
    9. Jalan, Akanksha & Matkovskyy, Roman & Urquhart, Andrew, 2022. "Demand elasticities of Bitcoin and Ethereum," Economics Letters, Elsevier, vol. 220(C).
    10. Ahmed, Walid M.A., 2022. "Robust drivers of Bitcoin price movements: An extreme bounds analysis," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

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    More about this item

    Keywords

    Cryptocurrency classification; Bitcoin; Volume-return relationships; Granger causality;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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