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Investigating the Dependence Structure of Dirty and Clean Cryptocurrencies with Energy and Green Assets: Evidence from Markov-Switching Models

Author

Listed:
  • Shu-Han Hsu

    (NTUB - National Taipei University of Business)

  • Yiwen Yang

    (NTNU - National Taiwan Normal University)

  • Po-Keng Cheng

    (Department of Finance and Cooperative Management, National Taipei University)

Abstract

The rapid growth of cryptocurrencies, characterized by high volatility, weak correlations with traditional assets, and substantial environmental impacts, has spurred interest in their interactions with energy and green financial markets. This study employs a Markov-switching generalized autoregressive conditional heteroscedasticity model with dynamic conditional correlation to examine the regime-dependent return and volatility connectedness between dirty and clean cryptocurrencies and energy and green financial assets. The empirical results reveal regime dependence in cryptocurrency dynamics, as well as differences in return and volatility connectedness across asset classes. These findings underscore the importance of accounting for regime-dependent dynamics when evaluating cross-market risk transmission and diversification properties between cryptocurrencies, energy markets, and green financial assets.

Suggested Citation

  • Shu-Han Hsu & Yiwen Yang & Po-Keng Cheng, 2026. "Investigating the Dependence Structure of Dirty and Clean Cryptocurrencies with Energy and Green Assets: Evidence from Markov-Switching Models," Working Papers hal-05470656, HAL.
    • Handle: RePEc:hal:wpaper:hal-05470656
    Note: View the original document on HAL open archive server: https://hal.science/hal-05470656v1
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