scholarly journals Why Does Stock Market Volatility Change Over Time?

10.3386/w2798 ◽  
1988 ◽  
Author(s):  
G. William Schwert
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Change Over Time ◽  
Over Time

scholarly journals Election Cycles and Stock Market Reaction: International Evidence

2015 ◽  
Vol 30 (3) ◽  
pp. 23-40
Author(s):  
Park Cheol Beom ◽  
An Ji Youn
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Dynamic Pattern ◽  
International Evidence ◽  
Stock Market Reaction ◽  
Before And After ◽  
Using Data ◽  
Over Time ◽  
Made In

This study investigates movements of stock market volatility during election periods (the six months before and after an election) using data from 16 countries. The main findings of this study are (1) volatility declines over time as elections approach, (2) the level of volatility during election periods is lower than that during nonelection periods, and (3) volatility rises quickly during election months and immediately after the elections. The first and second findings confirm assertions made in previous studies, such as Pantzalis, Stangeland, and Turtle (2000) and Wisniewski (2009), regarding the dynamic pattern of stock market volatility during election years.

scholarly journals Dynamic spillovers between stock and money markets in Nigeria: A VARMA-GARCH approach

2019 ◽  
Vol 11 (2) ◽  
pp. 255-283
Author(s):  
Afees A. Salisu ◽  
Kazeem O. Isah ◽  
Alberto Assandri
Keyword(s):  
Stock Market ◽  
Money Market ◽  
Conditional Variance ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Forecast Performance ◽  
Money Markets ◽  
Alternative Approach ◽  
Multivariate Volatility ◽  
Over Time

This study examines probable dynamic spillover transmissions between the Nigerian stock and money markets using the multivariate volatility framework that simultaneously accounts for both returns and shock spillovers. Based on relevant pre-tests, the VARMA-CCC-GARCH framework is selected and consequently employed to model the spillovers. The study finds significant cross-market return and shock spillovers between the two markets. Thus, a shock to one market is more likely to spill over to the other market. It is also observed that shocks have persistent effects on stock market volatility but transitory effects on money market volatility. In other words, shocks to the money market die out over time while shocks to stock market tend to persist over time. In addition, including lagged own shocks and lagged own conditional variance when forecasting the future volatility of both return series may enhance their forecast performance. An alternative approach proposed by Diebold and Yilmaz (2012) is also employed for robustness and the results are consistent with those obtained from the VARMA-CCC-GARCH model.

scholarly journals Volatility Modeling: An Overview of Equity Markets in the Euro Area during COVID-19 Pandemic

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1212
Author(s):  
Pierdomenico Duttilo ◽  
Stefano Antonio Gattone ◽  
Tonio Di Di Battista
Keyword(s):  
Stock Market ◽  
Euro Area ◽  
Risk Premium ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Volatility Modeling ◽  
The Impact ◽  
Second Wave ◽  
Daily Returns

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.

Stock Market Volatility and Terrorism: New Evidence from the Markov Switching Model

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Faheem Aslam ◽  
Hyoung-Goo Kang ◽  
Khurrum Shahzad Mughal ◽  
Tahir Mumtaz Awan ◽  
Yasir Tariq Mohmand
Keyword(s):  
Stock Market ◽  
Significant Risk ◽  
Market Volatility ◽  
Terrorist Attacks ◽  
Stock Market Volatility ◽  
Markov Switching Model ◽  
Physical Damage ◽  
Stock Market Returns ◽  
Switching Model ◽  
The Impact

AbstractTerrorism in Pakistan poses a significant risk towards the lives of people by violent destruction and physical damage. In addition to human loss, such catastrophic activities also affect the financial markets. The purpose of this study is to examine the impact of terrorism on the volatility of the Pakistan stock market. The financial impact of 339 terrorist attacks for a period of 18 years (2000–2018) is estimated w.r.t. target type, days of the week, and surprise factor. Three important macroeconomic variables namely exchange rate, gold, and oil were also considered. The findings of the EGARCH (1, 1) model revealed that the terrorist attacks targeting the security forces and commercial facilities significantly increased the stock market volatility. The significant impact of terrorist attacks on Monday, Tuesday, and Thursday confirms the overreaction of investors to terrorist news. Furthermore, the results confirmed the negative linkage between the surprise factor and stock market returns. The findings of this study have significant implications for investors and policymakers.

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