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Return and volatility spillover drivers among conventional cryptocurrencies

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  • Wafa Masmoudi Kammoun

    (IHEC- University of Carthage)

Abstract

This paper examines the return and volatility connectedness among major cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Dogecoin, Cardano (ADA), Binance Coin (BNB), and Tether (USDT)—while accounting for the influence of macro-financial uncertainty indices. Unlike previous studies that focused on static connectedness or examined only a limited set of uncertainty measures, our study offers a more comprehensive and up-to-date perspective on the conventional cryptocurrency market by analyzing both return and volatility connectedness within a unified framework. Using a TVP-VAR framework combined with quantile regressions, the study innovates by jointly examining the dynamic spillovers among cryptocurrencies and the heterogeneous effects of multiple macro-financial uncertainty indicators across different market conditions, thereby providing deeper insights into systemic risk transmission in digital asset markets. Empirical results indicate that the cryptocurrency market displays a moderate yet time-varying degree of interconnectedness, with Bitcoin and Ethereum serving as the main transmitters of return and volatility shocks, whereas Tether acts as a stabilizing receiver. The Total Connectedness Index increases markedly during major crises such as the COVID-19 pandemic and the Russia–Ukraine war, reflecting stronger systemic linkages under stress. Quantile regression results further show that the effects of uncertainty factors differ across market conditions: Economic Policy Uncertainty (EPU) and the Fear and Greed Index (FG) generally weaken connectedness, while market volatility (VIX) and Geopolitical Risk (GPR) amplify it. Investor Happiness (IH) enhances connectedness across most quantiles, and Infectious Disease Volatility (ID-EMV) tends to dampen spillovers, especially during turbulent periods. These findings highlight the importance of monitoring uncertainty indicators for risk management and portfolio allocation, as they offer novel insights into the systemic behavior of cryptocurrencies under stress and contribute to the broader literature on macro-financial spillovers in digital asset markets.

Suggested Citation

  • Wafa Masmoudi Kammoun, 2026. "Return and volatility spillover drivers among conventional cryptocurrencies," Digital Finance, Springer, vol. 8(1), pages 1-39, March.
    • Handle: RePEc:spr:digfin:v:8:y:2026:i:1:d:10.1007_s42521-025-00167-y
    DOI: 10.1007/s42521-025-00167-y
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    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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