The study used a DCC-GARCH model with R² decomposed connectedness measures to examine how volatility was transmitted among the nine assets. This framework estimates time-varying conditional relationships and separates spillovers into directional components. The results characterize associations within the selected markets and currencies; they do not establish that any asset caused another’s price or volatility changes.

Dynamic conditional correlations differed clearly by digital-currency type. Bitcoin and Ethereum were strongly correlated with each other, while the two stablecoins included in the analysis—Tether USD and USD Coin—showed negative correlations with other assets. China’s stock market was described as largely insulated from the global markets in the study, distinguishing it from the more closely linked national stock markets.

Despite those differences, the decomposed connectedness analysis identified substantial spillovers across the overall system. China had the weakest connectedness of the five stock markets, whereas the United States acted as the primary risk transmitter. At the asset level, Bitcoin, the United States and Germany were classified as net transmitters of shocks, indicating that they contributed more to measured volatility transmission than they received from the other assets examined.

China, Japan and the United Kingdom were classified as net receivers. These categories describe the direction of spillovers produced by the model, not the performance of the assets or the direction of their returns. The findings also indicate that grouping all digital currencies together would obscure meaningful distinctions within the sample: Bitcoin and Ethereum formed a closely connected pair, while the stablecoins displayed different conditional relationships from the rest of the system.

The dynamic total connectedness index showed that the overall strength of cross-market volatility links was not constant. It reached three notable peaks in early 2019, during the first half of 2020 and in mid-2023. The researchers associated these periods with the 2019 cryptocurrency crash, the COVID-19 pandemic and regulatory changes in 2023, respectively, but presented them as possible explanations rather than demonstrated causes.

After the mid-2023 peak, the total connectedness index entered a downward trajectory through the end of the observation window in October 2025. In the model’s terms, the measured volatility connections among the selected cryptocurrencies and stock markets weakened over that phase. This movement should not be interpreted as proof of a general reduction in global financial interdependence, because the result applies specifically to the assets, dates and model specification analyzed.

Several limits define the study’s scope. It relies on weekly observations, covers only four digital currencies and five national stock markets, and captures time-varying associations rather than universal or permanent relationships. Its transmitter and receiver classifications likewise depend on the model and the period examined. Even so, the analysis offers a differentiated view of cross-market volatility: Bitcoin and Ethereum were closely linked, stablecoins showed contrasting correlations, the United States and Germany transmitted more measured shocks than they absorbed, and China, Japan and the United Kingdom received more than they transmitted.

Subject of Research: Time-varying volatility spillovers between major cryptocurrencies and national stock markets

Article Title: The volatility spillover between digital currencies and stock markets:evidence from DCC-GARCH R2 decomposed connectedness measures

Article References: Chen, Y., & Luo, P. (2026). The volatility spillover between digital currencies and stock markets:evidence from DCC-GARCH R2 decomposed connectedness measures. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09419-7

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09419-7

Keywords: cryptocurrencies, stock markets, volatility spillovers, DCC-GARCH, connectedness analysis, Bitcoin, Ethereum, stablecoins, cross-market risk, time-varying correlation, volatility, spillover