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Frequency domain causality and quantile connectedness between investor sentiment and cryptocurrency returns

Author

Listed:
  • Zhu, Huiming
  • Xing, Zhanming
  • Ren, Yinghua
  • Chen, Yiwen
  • Hau, Liya

Abstract

This study investigates the frequency-domain causality and quantile connectedness between online investors’ fear sentiment and cryptocurrency returns. We propose cross-quantile coherency and networks to examine the frequency-domain nonlinear interdependence. First, we find that investor fear sentiment and cryptocurrency returns exhibit bidirectional causality. Second, fear exhibits an asymmetric connectedness with cryptocurrency returns across quantiles and frequencies. Third, short-term cross-quantile connectedness is found to be more significant than long-term connectedness. These findings can help investors and policymakers make decisions regarding diversified hedging and controlling for potential risks.

Suggested Citation

  • Zhu, Huiming & Xing, Zhanming & Ren, Yinghua & Chen, Yiwen & Hau, Liya, 2023. "Frequency domain causality and quantile connectedness between investor sentiment and cryptocurrency returns," International Review of Economics & Finance, Elsevier, vol. 88(C), pages 1035-1051.
    • Handle: RePEc:eee:reveco:v:88:y:2023:i:c:p:1035-1051
    DOI: 10.1016/j.iref.2023.07.038
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    References listed on IDEAS

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    1. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    2. Muhammad MOHSIN & Sobia NASEEM & Larisa IVAȘCU & Lucian-Ionel CIOCA & Muddassar SARFRAZ & Nicolae Cristian STĂNICĂ, 2021. "Gauging the Effect of Investor Sentiment on Cryptocurrency Market: An Analysis of Bitcoin Currency," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(4), pages 87-102, December.
    3. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
    4. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    5. Westerhoff, Frank H., 2004. "Greed, fear and stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 343(C), pages 635-642.
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    Cited by:

    1. Ghaemi Asl, Mahdi & Ben Jabeur, Sami & Hosseini, Seyedeh Sana & Tajmir Riahi, Hamed, 2024. "Fintech's impact on conventional and Islamic sustainable equities: Short- and long-term contributions of the digital financial ecosystem," Global Finance Journal, Elsevier, vol. 62(C).
    2. Abakah, Emmanuel Joel Aikins & Brahim, Mariem & Carlotti, Jean-Etienne & Tiwari, Aviral Kumar & Mensi, Walid, 2024. "Extreme downside risk connectedness and portfolio hedging among the G10 currencies," International Economics, Elsevier, vol. 178(C).

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    More about this item

    Keywords

    Investor sentiment; Fears; Cryptocurrency returns; Frequency domain causality; Cross-quantile coherency; Cross-quantile network;
    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
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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