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Are Google searches making the Bitcoin market run amok? A tail event analysis

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  • Neto, David

Abstract

This paper aims at detecting extreme value spillover between the large co-movements of Bitcoin returns and the rate of change in investor attention (for which Google search is used as a proxy). For this purpose, we use the concept of the Granger causality in tail event. Thus, we test whether positive, or negative, extreme values of rate of change in Google searches have a significant predictive power for negative, or positive, large values of Bitcoin returns, and vice versa . Our results shed light on a unidirectional causality effect from the returns to investor attention in the first place, before becoming bidirectional when the time delay increases.

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  • Neto, David, 2021. "Are Google searches making the Bitcoin market run amok? A tail event analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:ecofin:v:57:y:2021:i:c:s1062940821000796
    DOI: 10.1016/j.najef.2021.101454
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    Cited by:

    1. Neto, David, 2022. "Revisiting spillovers between investor attention and cryptocurrency markets using noisy independent component analysis and transfer entropy," The Journal of Economic Asymmetries, Elsevier, vol. 26(C).
    2. Koch, Sophia & Dimpfl, Thomas, 2023. "Attention and retail investor herding in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 51(C).
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    4. Neto, David, 2022. "Examining interconnectedness between media attention and cryptocurrency markets: A transfer entropy story," Economics Letters, Elsevier, vol. 214(C).
    5. Dora Almeida & Andreia Dionísio & Paulo Ferreira & Isabel Vieira, 2023. "Impact of the COVID-19 Pandemic on Cryptocurrency Markets: A DCCA Analysis," FinTech, MDPI, vol. 2(2), pages 1-17, May.

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    More about this item

    Keywords

    Bitcoin; Media attention; Quantile-dependent measure of dependence; Conditional autoregressive quantile model; Granger causality in tail event;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G1 - Financial Economics - - General Financial Markets

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