Analyzing the interconnections between clean and dirty cryptocurrency and energy markets

【Author】 Zhou, Xiaoguang; Guo, Xueyao; Chen, Yanan

【Source】PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS

【影响因子】3.778

【Abstract】With the increasing global attention to environmental sustainability, the application of clean energy and the energy efficiency of the cryptocurrency market have become increasingly important. This paper uses weekly data from March 2018 to July 2024 on the clean cryptocurrency market, dirty cryptocurrency market, and the energy market. It applies an autoencoder for dimensionality reduction of five clean cryptocurrency markets and four dirty cryptocurrency markets to calculate the corresponding indices. Subsequently, an R-vine copula model is constructed to analyze the structural relationships and correlations among the three markets. The study then constructs a vector autoregressive (VAR) model and conducts Granger causality tests and Diebold & Yilmaz variance decomposition to analyze the risk spillovers between the markets. The results indicate that solar energy plays a central role in the energy sector, bridging the gap between clean and dirty energy. Ethereum (ETH), through its blockchain technology and decentralized applications, has become an important link connecting the energy market, clean cryptocurrency market, and dirty cryptocurrency market. Furthermore, there exists a significant asymmetry in risk spillovers between the cryptocurrency market and the energy market, with both short-term and long-term spillovers showing similar asymmetries. Coal, wind energy, and solar energy act as risk receivers, while cryptocurrencies, crude oil, and natural gas primarily contribute to risk. Investors should focus on oil and cryptocurrency markets, less affected by others, while regulators should implement tailored strategies for solar and wind energy markets, which react more strongly to external risks, to prevent volatility.

【Keywords】Cryptocurrency market; Energy market; R -vine copula; Vector autoregressive; Risk measurement

【发表时间】2025 SEP 15

【收录时间】2025-06-28

【文献类型】

【主题类别】

--