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Interest in cryptocurrencies predicts conditional correlation dynamics

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  • Chuffart, Thomas

Abstract

Using a Smooth Transition Conditional Correlation model with Google search data as a transition variable, I investigate correlation dynamics between a set of crypto-currencies. A major change in the correlation dynamics after the 2017 bubble burst is explained by the attention subsequently surrounding cryptocurrencies. Google searches are found to be a good predictor of correlation between cryptocurrencies and could provide useful input to portfolio management.

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  • Chuffart, Thomas, 2022. "Interest in cryptocurrencies predicts conditional correlation dynamics," Finance Research Letters, Elsevier, vol. 46(PA).
    • Handle: RePEc:eee:finlet:v:46:y:2022:i:pa:s1544612321002956
    DOI: 10.1016/j.frl.2021.102239
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    Cited by:

    1. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    2. Bonaparte, Yosef & Bernile, Gennaro, 2023. "A new “Wall Street Darling?” effects of regulation sentiment in cryptocurrency markets," Finance Research Letters, Elsevier, vol. 52(C).
    3. Bouri, Elie & Kamal, Elham & Kinateder, Harald, 2023. "FTX Collapse and systemic risk spillovers from FTX Token to major cryptocurrencies," Finance Research Letters, Elsevier, vol. 56(C).

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