Day of the week effect and stock market volatility in Ghana and Nairobi stock exchanges

2017 ◽  
Vol 42 (4) ◽  
pp. 727-745 ◽  
Author(s):  
James Mark Gbeda ◽  
James Atta Peprah
2013 ◽  
Vol 29 (6) ◽  
pp. 1727 ◽  
Author(s):  
Omar Farooq ◽  
Mohammed Bouaddi ◽  
Neveen Ahmed

This paper investigates the day of the week effect in the volatility of the Saudi Stock Exchange during the period between January 7, 2007 and April 1, 2013. Using a conditional variance framework, we find that the day of the week effect is present in the volatility. Our results show that the lowest volatility occurs on Saturdays and Sundays. We argue that due to the closure of international markets on Saturdays and Sundays, there is not enough activity in the Saudi Stock Exchange. As a result, the volatility is the lowest on these days. Our results also show that the highest volatility occurs on Wednesdays. We argue Wednesday, being the last trading day of the week, corresponds with the start of four non-trading days (Thursday through Sunday) for foreign investors. Fearing that they will be stuck up with stocks in case some unfavorable information enters the market, foreign investors tend to exit the market on Wednesdays. As a result of excessive trading, there is high volatility on Wednesdays.


2001 ◽  
Vol 25 (2) ◽  
pp. 181-193 ◽  
Author(s):  
Hakan Berument ◽  
Halil Kiymaz

2011 ◽  
Vol 3 (9) ◽  
pp. 331-333 ◽  
Author(s):  
Ramona Birău ◽  
◽  
Jatin Trivedi

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista

Volatility is the most widespread measure of risk. Volatility modeling allows investors to capture potential losses and investment opportunities. This work aims to examine the impact of the two waves of COVID-19 infections on the return and volatility of the stock market indices of the euro area countries. The study also focuses on other important aspects such as time-varying risk premium and leverage effect. This investigation employed the Threshold GARCH(1,1)-in-Mean model with exogenous dummy variables. Daily returns of the euro area stock markets indices from 4th January 2016 to 31st December 2020 has been used for the analysis. The results reveal that euro area stock markets respond differently to the COVID-19 pandemic. Specifically, the first wave of COVID-19 infections had a notable impact on stock market volatility of euro area countries with middle-large financial centres while the second wave had a significant impact only on stock market volatility of Belgium.


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