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Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis

Author

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  • Parthajit Kayal

    (Madras School of Economics (MSE), Behind Government Data Centre)

  • Sumanjay Dutta

    (Indian Institute of Science)

Abstract

This paper investigates the volatility dynamics of Bitcoin, Ethereum, and Litecoin both before and after the COVID-19 pandemic. Employing an asymmetric two-state MS-MGARCH model, we identify a regime change in their volatility dynamics, providing a foundation for examining pre- and post-COVID volatility spillovers. Using the BEKK-GARCH model, we statistically confirm a spillover and quantify the correlation strength among the cryptocurrencies through the Windowed Scalogram Difference approach. Additionally, the cross-quantilogram approach is utilized to identify potential causal networks among the cryptocurrencies. The study unveils bidirectional shock transmission and volatility spillovers between Bitcoin and Ethereum, as well as Bitcoin and Litecoin, underscoring their interconnected nature. Furthermore, bidirectional volatility connections between Litecoin and Ethereum are uncovered. These findings enhance our comprehension of cryptocurrency volatility, emphasizing the influence of historical shocks and prior volatility on present dynamics. The study has practical implications for investors, traders, and policymakers, offering valuable insights for risk management in the cryptocurrency market, thus contributing to the advancement of knowledge in cryptocurrency dynamics and supporting more informed decision-making in this continually evolving financial landscape.

Suggested Citation

  • Parthajit Kayal & Sumanjay Dutta, 2024. "Regime switching and causal network analysis of cryptocurrency volatility: evidence from pre-COVID and post-COVID analysis," Digital Finance, Springer, vol. 6(2), pages 319-340, June.
    • Handle: RePEc:spr:digfin:v:6:y:2024:i:2:d:10.1007_s42521-023-00104-x
    DOI: 10.1007/s42521-023-00104-x
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    Keywords

    Volatility; Cryptocurrencies; COVID-19; Spillovers; Correlation; Causal networks;
    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
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets
    • 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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