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).

개념적 집중형 일 유출모형인 DAWAST모형을 선정하여 용수수요를 고려할 수 있는 가능성을 검토하였다. 기존 모형에 의한 모의 유량을 자연유량으로 보았고, 농업용수, 생활용수, 공업용수 등 회귀수량을 더한 값을 하천유량으로 가정하였다. 농업용수의 수요량은 회귀수량이 논으로부터만 발생하는 것으로 보아 논 용수량만을 고려하였으며, 수정 Penman공식에 의한 증발산량, 침투량, 재배관리수량, 유효우량 등을 고려한 일별 감수심에 의해 일별로 계산하였다. 생활