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Returns and volume: Frequency connectedness in cryptocurrency markets

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  • Fousekis, Panos
  • Tzaferi, Dimitra

Abstract

The present paper investigates the causal link between returns and volume in cryptocurrency markets. Relative to earlier empirical studies on the topic, it employs a flexible methodological approach that allows the intensity and the pattern of the link to vary with the traders’ planning horizon (shorter vs longer runs) and the nature of the preceding price shocks (positive vs negative). Causality is bi-directional implying that: (a) volume-based technical analysis can lead to higher profits and (b) trend-following is likely to be a common investment strategy among rational uninformed traders. There is, therefore, scope for establishing appropriate disclosure systems to deal with information asymmetry and the risk associated with it. In the longer-run, spillovers from returns to volume are stronger than those in the opposite direction. The same, generally, applies to spillovers from positive relative to negative returns. Consequently, returns (especially the positive ones) are more likely to induce fundamental changes in traders’ expectations compared to volume.

Suggested Citation

  • Fousekis, Panos & Tzaferi, Dimitra, 2021. "Returns and volume: Frequency connectedness in cryptocurrency markets," Economic Modelling, Elsevier, vol. 95(C), pages 13-20.
  • Handle: RePEc:eee:ecmode:v:95:y:2021:i:c:p:13-20
    DOI: 10.1016/j.econmod.2020.11.013
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    Cited by:

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    4. Mudassar Hasan & Muhammad Abubakr Naeem & Muhammad Arif & Syed Jawad Hussain Shahzad & Xuan Vinh Vo, 2022. "Liquidity connectedness in cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    5. Balcilar, Mehmet & Ozdemir, Huseyin & Agan, Busra, 2022. "Effects of COVID-19 on cryptocurrency and emerging market connectedness: Empirical evidence from quantile, frequency, and lasso networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    6. Kumar, Ashish & Iqbal, Najaf & Mitra, Subrata Kumar & Kristoufek, Ladislav & Bouri, Elie, 2022. "Connectedness among major cryptocurrencies in standard times and during the COVID-19 outbreak," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    7. Bojaj, Martin M. & Muhadinovic, Milica & Bracanovic, Andrej & Mihailovic, Andrej & Radulovic, Mladen & Jolicic, Ivan & Milosevic, Igor & Milacic, Veselin, 2022. "Forecasting macroeconomic effects of stablecoin adoption: A Bayesian approach," Economic Modelling, Elsevier, vol. 109(C).
    8. Mensi, Walid & Al-Yahyaee, Khamis Hamed & Wanas Al-Jarrah, Idries Mohammad & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Does volatility connectedness across major cryptocurrencies behave the same at different frequencies? A portfolio risk analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 96-113.
    9. Jinxin Cui & Aktham Maghyereh, 2022. "Time–frequency co-movement and risk connectedness among cryptocurrencies: new evidence from the higher-order moments before and during the COVID-19 pandemic," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-56, December.
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    13. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    14. Chowdhury, Md Shahedur R. & Damianov, Damian S. & Elsayed, Ahmed H., 2022. "Bubbles and crashes in cryptocurrencies: Interdependence, contagion, or asset rotation?," Finance Research Letters, Elsevier, vol. 46(PB).
    15. Rubbaniy, Ghulame & Polyzos, Stathis & Rizvi, Syed Kumail Abbas & Tessema, Abiot, 2021. "COVID-19, Lockdowns and herding towards a cryptocurrency market-specific implied volatility index," Economics Letters, Elsevier, vol. 207(C).
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    17. Al-Shboul, Mohammad & Assaf, Ata & Mokni, Khaled, 2022. "When bitcoin lost its position: Cryptocurrency uncertainty and the dynamic spillover among cryptocurrencies before and during the COVID-19 pandemic," International Review of Financial Analysis, Elsevier, vol. 83(C).

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    More about this item

    Keywords

    Cryptocurrencies; Volume; Returns; Frequency; Causation;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General

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