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Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis

Author

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  • Li, Rong
  • Li, Sufang
  • Yuan, Di
  • Zhu, Huiming

Abstract

This paper investigates the relationship between investor attention and the major cryptocurrency markets by wavelet-based quantile Granger causality. The wavelet analysis illustrates the interdependence between investor attention and the cryptocurrency returns. Multi-scale quantile Granger causality based on wavelet decomposition further demonstrates bidirectional Granger causality between investor attention and the returns of Bitcoin, Ethereum, Ripple and Litecoin for all quantiles, except for the medium. Among them, the Granger causality from investor attention to the returns is relatively very weak for Ethereum. In the short term, the Granger causality from these cryptocurrency returns to investor attention seems symmetric, but in the medium- and long- term, the causality shows some asymmetry. The Granger causality from investor attention to these cryptocurrency returns is asymmetric and varies across cryptocurrencies and time scales. Specifically, investor attention has a relatively stronger impact on the cryptocurrency returns in bearish markets than that in bullish markets in the short term.

Suggested Citation

  • Li, Rong & Li, Sufang & Yuan, Di & Zhu, Huiming, 2021. "Investor attention and cryptocurrency: Evidence from wavelet-based quantile Granger causality analysis," Research in International Business and Finance, Elsevier, vol. 56(C).
    • Handle: RePEc:eee:riibaf:v:56:y:2021:i:c:s0275531921000106
    DOI: 10.1016/j.ribaf.2021.101389
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    Citations

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    Cited by:

    1. Kamal, Javed Bin & Hassan, M. Kabir, 2022. "Asymmetric connectedness between cryptocurrency environment attention index and green assets," The Journal of Economic Asymmetries, Elsevier, vol. 25(C).
    2. Ayadi, Ahmed & Ghabri, Yosra & Guesmi, Khaled, 2023. "Directional predictability from central bank digital currency to cryptocurrencies and stablecoins," Research in International Business and Finance, Elsevier, vol. 65(C).
    3. Liu, Jian & Julaiti, Jiansuer & Gou, Shangde, 2024. "Decomposing interconnectedness: A study of cryptocurrency spillover effects in global financial markets," Finance Research Letters, Elsevier, vol. 61(C).
    4. Chu, Meifen, 2021. "Bitcoin and traditional currencies during the Covid-19 pandemic period," MPRA Paper 110117, University Library of Munich, Germany.
    5. Chen, Qitong & Zhu, Huiming & Yu, Dongwei & Hau, Liya, 2022. "How does investor attention matter for crude oil prices and returns? Evidence from time-frequency quantile causality analysis," The North American Journal of Economics and Finance, Elsevier, vol. 59(C).
    6. Wang, Lu & Ruan, Hang & Lai, Xiaodong & Li, Dongxin, 2024. "Economic extremes steering renewable energy trajectories: A time-frequency dissection of global shocks," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    7. 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).
    8. Deng, Chao & Zhou, Xiaoying & Peng, Cheng & Zhu, Huiming, 2022. "Going green: Insight from asymmetric risk spillover between investor attention and pro-environmental investment," Finance Research Letters, Elsevier, vol. 47(PA).
    9. Moinak Maiti & Parthajit Kayal, 2022. "Asymmetric Information Flow between Exchange Rate, Oil, and Gold: New Evidence from Transfer Entropy Approach," JRFM, MDPI, vol. 16(1), pages 1-14, December.

    More about this item

    Keywords

    Cryptocurrency; Investor attention; Granger causality; Wavelet; Quantile regression;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G40 - Financial Economics - - Behavioral Finance - - - General

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