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The impact of bitcoin fear and greed on good and bad network connectedness: the case of the US sectoral high frequency returns

Author

Listed:
  • Muhammad Tahir Suleman

    (University of Otago)

  • Umaid A Sheikh

    (University of Otago)

  • Emilios C. Galariotis

    (Audencia Business School)

  • David Roubaud

    (Montpellier Business School)

Abstract

This article is the first one to examine the moderating role of bitcoin sentiment indices on the short term and long-term time–frequency-based good and bad network connectedness of all US sectors. In more detail, the paper quantifies the above relationship between the 11 US sectoral high frequency returns and then identifies the moderating impact of bitcoin investors’ fear and greed sentiment on good and bad network connectedness during pre-Covid-19 and Covid-19. For the said purpose, we decompose the returns into good and bad volatility, and rely on time and frequency dependent spillover measures and quantify a spillover symmetrical and asymmetrical measure for network connectedness for different investment horizons. Furthermore, we also quantify the NET good–bad volatility transmission and reception capability of all our sectors within the frequency dependent network. The extracted good and bad network connectedness indices are then regressed on multiple thresholds of bitcoin sentiment indices. Quantile regression results revealed that fear, extreme fear, greed and extreme greed moderate the short term and long term good and bad volatility spillovers within the network connectedness. Finally, we also utilize hedge ratios and optimal portfolio weight selection strategies to explain whether short positioning in the US sectoral returns can be used to hedge against bitcoin sentiment risk.

Suggested Citation

  • Muhammad Tahir Suleman & Umaid A Sheikh & Emilios C. Galariotis & David Roubaud, 2025. "The impact of bitcoin fear and greed on good and bad network connectedness: the case of the US sectoral high frequency returns," Annals of Operations Research, Springer, vol. 347(1), pages 633-677, April.
    • Handle: RePEc:spr:annopr:v:347:y:2025:i:1:d:10.1007_s10479-023-05455-7
    DOI: 10.1007/s10479-023-05455-7
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    Keywords

    Financial markets; Network modelling; Asymmetrical network connectedness; Time–frequency dependent US sectoral network; Bitcoin sentiment indexes; Portfolio diversification; Realized and semi-realized variances; Covid-19; Hedge ratios; Optimal portfolio weight selection strategy; High frequency data;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G4 - Financial Economics - - Behavioral Finance
    • G40 - Financial Economics - - Behavioral Finance - - - General
    • G41 - Financial Economics - - Behavioral Finance - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making in Financial Markets

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