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Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness

Author

Listed:
  • Jan Sila

    (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia & Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czechia)

  • Evzen Kocenda

    (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia & Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czechia)

  • Ladislav Kristoufek

    (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia & Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czechia)

  • Jiri Kukacka

    (Charles University, Faculty of Social Sciences, Institute of Economic Studies, Prague, Czechia & Czech Academy of Sciences, Institute of Information Theory and Automation, Prague, Czechia)

Abstract

Cryptocurrencies exhibit unique statistical and dynamic properties compared to those of traditional financial assets, making the study of their volatility crucial for portfolio managers and traders. We investigate the volatility connectedness dynamics of a representative set of eight major crypto assets. Methodologically, we decompose the measured volatility into positive and negative components and employ the time-varying parameters vector autoregression (TVP-VAR) framework to show distinct dynamics associated with market booms and downturns. The results suggest that crypto connectedness reflects important events and exhibits more variable and cyclical dynamics than those of traditional financial markets. Periods of extremely high or low connectedness are clearly linked to specific events in the crypto market and macroeconomic or monetary history. Furthermore, existing asymmetry from good and bad volatility indicates that information about market downturns spills over substantially faster than news about comparable market surges. Overall, the connectedness dynamics are predominantly driven by fundamental crypto factors, while the asymmetry measure also depends on macro factors such as the VIX index and the expected inflation.

Suggested Citation

  • Jan Sila & Evzen Kocenda & Ladislav Kristoufek & Jiri Kukacka, 2023. "Good vs. Bad Volatility in Major Cryptocurrencies: The Dichotomy and Drivers of Connectedness," Working Papers IES 2023/24, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, revised Jul 2023.
    • Handle: RePEc:fau:wpaper:wp2023_24
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    2. Yin, Wei & Wu, Fan & Zhou, Peng & Kirkulak-Uludag, Berna, 2025. "Exploring resilience in the cryptocurrency market: Risk transmission and network robustness," International Review of Financial Analysis, Elsevier, vol. 106(C).
    3. Shahzad, Syed Jawad Hussain & Hoang, Thi Hong Van & Caporin, Massimiliano & Naifar, Nader, 2026. "Volatility spillovers in forex markets and the role of quantitative easing," The North American Journal of Economics and Finance, Elsevier, vol. 81(C).

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    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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