ABSTRACT
The ecological structure of the cryptocurrency market and its external environmental impact cannot be ignored. Taking Bitcoin as an example, the study analyzes the Bitcoin market during the COVID-19 pandemic crisis from an environmental perspective based on the differences in the political and economic systems of China and the United States. First, the institutional environment selected in this study is used as an indicator of environmental measurement and divided into formal and informal sectors. In the analysis, the former selects the economic policy uncertainty index of China, while the latter selects bitcoin’s social attention (including Google trends and the Baidu index) and investor sentiment as indicators. Second, financial market data such as foreign exchange and commodity futures are used as indicators for analyzing the environment of the bitcoin market. Using VAR modeling analysis, the results show that both the institutional and the market environment have an impact on bitcoin’s market performance.
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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Zhunzhun Liu
Zhunzhun Liu is currently a postdoctoral fellow at the Post-Doctoral Research Centre of the Shanghai Gold Exchange, which is a joint postdoctoral programme with the School of Management of Fudan University. She has a Ph.D. degree in Business Management from Zhejiang University. This paper is a subtopic of her doctoral thesis. Her research interests include Financial Market Infrastructure and FinTech, which focuses on third-party payments and cryptocurrencies.
Lu-Xi Zou
Lu-Xi Zou is a senior engineer, associate professor at the School of Management, Xuzhou Medical University. As first or corresponding author, he has more than twenty papers published in important journals with impact factors and Journal Citation Reports in Web of Sciences. He has experience in the area of Information Management. Her research interests are Information Economy, Fintech, and Machine Learning.