Past literatures have not studied the impact of real-world events or information on the return and volatility of virtual currencies, particularly on the COVID-19 event, day-of-the-week effect, daily high-low price spreads and information flow rate. The study uses the ARMAGARCH model to capture Bitcoin’s return and conditional volatility, and explores the impact of information flow rate on conditional volatility in the Bitcoin market based on the Mixture Distribution Hypothesis (Clark, 1973). There were 3,064 samples collected during the period from 1st of January 2012 to 20th April, 2020. Empirical results show that in the Bitcoin market, a daily high-low price spread has a significant inverse relationship for daily return, and information flow rate has a significant positive relationship for condition volatility. The study supports a significant negative relationship between information asymmetry and daily return, and there is a significant positive relationship between daily trading volume and condition volatility. When Bitcoin trades on Saturday & Sunday, there is a significant reverse relationship for conditional volatility and there exists a day-of-the-week volatility effect. Under the impact of COVID-19 event, Bitcoin’s condition volatility has increased significantly, indicating the risk of price changes. Finally, the Bitcoin’s return has no impact on COVID-19 events and holidays (Saturday & Sunday).

Abstract
1. Introduction
2. Research Methodologies
    2.1. MDH
    2.2. Empirical Model
    2.3. Null Hypothesis
3. Data and Empirical Results
4. Conclusions
References

• Ying Sing LIU(College of Humanities and Social Sciences, Chaoyang University of Technology, Taiwan) Corresponding Author
• Liza LEE(College of Humanities and Social Sciences, Chaoyang University of Technology, Taiwan)