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Assessing Cryptomarket Risks: Macroeconomic Forces, Market Shocks and Behavioural Dynamics

Author

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  • Josué Thélissaint

    (Univ Rennes, CNRS, CREM – UMR6211, F-35000 Rennes, France)

Abstract

This paper aims at exploring risk factors which are driving forces behind the global cryptomarket behaviour. Its purpose is to enhance understanding of the transmission mechanisms of aggregated fluctuations. Identification of such factors will contribute to laying foundation for anchoring expectations from a forward-looking perspective. We use the Factor-Augmented Autoregression (FAVAR) framework to estimate the latent factors. Nevertheless due to asymmetries, assessments are performed through a Threshold-VAR which helps to highlight predictive power of the factors. Six leading risk factors are identified. Each of them represents distinct market risks and exerts asymmetric effects on cryptomarket dynamics. Especially, the first factor (F1) encapsulates global market risks associated with volatility and collapse uncertainty. It orchestrates regime transitions among low−, medium−, and high − risk states. The sixth factor (F6) reflects market optimism toward cryptos. It shows a notable negative correlation (− 37%) with F1 over 20 business days. While F1 demonstrates high persistence, other factors exhibit mean-reverting behaviour. Furthermore, our findings are complemented by insights into the structure of shock transmission across different time horizons, highlighting the joint influence of macroeconomic and emotional shocks on market trajectories. Overall, this paper contributes to the existing literature as it offers a novel perspective on risk factors in cryptomarkets and it underscores specific issues for further research.

Suggested Citation

  • Josué Thélissaint, 2024. "Assessing Cryptomarket Risks: Macroeconomic Forces, Market Shocks and Behavioural Dynamics," Economics Working Paper Archive (University of Rennes & University of Caen) 2024-14, Center for Research in Economics and Management (CREM), University of Rennes, University of Caen and CNRS.
  • Handle: RePEc:tut:cremwp:2024-14
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    More about this item

    Keywords

    cryptomarkets; common risk factor; factor-augmented vector auto-regression; nonlinear impulse response function; time-varying frequency connectednes;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • 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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