• Journal of Distribution Science
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• v.15 no.8
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• pp.5-14
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• 2017

Purpose - This study empirically analyses the sale changes in large retail stores directly resulting from increased controls on those stores. More specifically, we discuss the economic impacts on Korean regulations that restrict trading hours and mandate statutory store closure 'holidays' twice per month. Research design, data and methodology - we attempt to empirically analyse the economic effects of trading hours regulations through quantitative analysis of the sales revenue data of large retail stores. We introduce the data and methods of empirical analysis used to analyse the economic effects of trading-hour regulations on large retail stores. We use a panel regression to analyse the sales losses of large retail stores caused by the new constraints on business hours. Results - The results of this study show that the sales of large retail stores fell by the average of 3.4% per month during the regulation periods. However, regulations affecting large retail stores have various economic impacts, including variations in sales, changes in consumption patterns, and influences on consumer welfare and national economy. Conclusions - Such changes may also be captured by other metrics: accordingly, further researches are needed to measure the impact of regulations on economic indicators such as employment and GDP.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.45-53
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• 2020

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 ARMA-GARCH 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 1^st of January 2012 to 20^th 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).