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.

scholarly journals Conditional volatility nexus between stock markets and macroeconomic variables

2018 ◽  
Vol 45 (1) ◽  
pp. 77-99 ◽  
Author(s):  
Ghulam Abbas ◽  
David G. McMillan ◽  
Shouyang Wang
Keyword(s):  
Stock Market ◽  
Stock Markets ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Macroeconomic Factors ◽  
Stock Market Returns ◽  
Content Type ◽  
Macroeconomic Variables ◽  
Volatility Transmission ◽  
The Impact

Purpose The purpose of this paper is to analyse the relation between stock market volatility and macroeconomic fundamentals for G-7 countries using monthly data over the period from July 1985 to June 2015. Design/methodology/approach The empirical methodology is based on two steps: in the first step, the authors obtain the conditional volatilities of stock market returns and macroeconomic variables through the GARCH family of models. The authors also incorporate the impact of early 2000s dotcom and the global financial crises. In the second step, the authors estimate multivariate vector autoregressive model to analyze the dynamic relation between stock markets return and macroeconomic variables. Findings The overall results for G-7 countries indicate a weak volatility transmission from macroeconomic factors to stock market volatility at individual level but the collective impact of volatility transmission is highly significant. Although, the results of block exogeneity indicate a bidirectional causality except UK, but the causal linkage is quite weak from stock market to macroeconomic variables. Moreover, the local financial variables excluding interest rate are closely integrated, and the volatility of industrial production growth and oil price are identified as the most significant macroeconomic factors that could possibly influence the directions of stock markets. Originality/value This research establishes the nature of the links between stock market and macroeconomic volatility. Research to date has been unable to satisfactorily establish the empirical nature of such links. The authors believe this paper begins to do this.

Macroeconomic Uncertainty and Stock Market Uncertainty: Some Further Evidence From Pakistan

2021 ◽  
pp. 2150014
Author(s):  
Tariq Aziz ◽  
Jahanzeb Marwat ◽  
Sheraz Mustafa
Keyword(s):  
Stock Market ◽  
Money Supply ◽  
Industrial Production ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Uncertainty ◽  
Market Returns ◽  
Macroeconomic Factors ◽  
Stock Market Returns ◽  
The Impact

The paper provides an updated evidence of the linkage between stock market and macroeconomic factors in Pakistan. The sample period is from January 2011 to November 2017. Macroeconomic variables used are money supply, exchange rate, treasury bill rate, inflation and industrial production. Generalized autoregressive conditional heteroscedasticity (GARCH) models have been used to examine the impact of macroeconomic factors on stock market return and stock market volatility. Findings suggest that macroeconomic factors have an impact on stock market volatility. The fluctuations in inflation and money supply negatively influence the volatility of stock market returns. In contrast, industrial production positively affects the fluctuations of stock market returns. The findings are important for shareholders, investors, regulatory authorities and policymakers.

scholarly journals The Measurement Method of Investor Sentiment and Its Relationship with Stock Market

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jiangshan Hu ◽  
Yunyun Sui ◽  
Fang Ma
Keyword(s):  
Stock Market ◽  
Stock Returns ◽  
Investor Sentiment ◽  
Principal Component ◽  
Market Volatility ◽  
Response Analysis ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Impact

Investor sentiment is a hot topic in behavioral finance. How to measure investor sentiment? Is the influence of investor sentiment on the stock market symmetrical? That is all we need to think about. Therefore, this paper firstly selects five emotional proxy variables and constructs an investor sentiment composite index by principal component analysis. Secondly, the MS-VAR model is employed to study the dynamic relationship among investor sentiment, stock market returns, and volatility. Using the model MSIH (2)-VAR (2), we found that the relationship among the investor sentiment, stock returns, and volatility is different in different regimes. The results of orthogonal cumulative impulse response analysis showed that the shock to investor sentiment has a significant impact on stock market returns, and this impact in the bullish stock market is significantly higher than in the bearish stock market. The impact of the shock to stock market returns on investor sentiment and stock market volatility is relatively significant. The shock to stock market volatility has significant effects on the stock market returns. Overall, the influence of investor sentiment on the stock market is asymmetric; that is, in different regimes of the stock market, the impact of investor sentiment on the stock market is different. Realizing this, investors can better understand and grasp the market, guiding their own investment behavior. Other researchers can also further study the measurement of investor sentiment on this basis to better guide investors’ behavior.

scholarly journals GARCH (1,1) Models and Analysis of Stock Market Turmoil during COVID-19 Outbreak in an Emerging and Developed Economy

2021 ◽  
Vol 14 (12) ◽  
pp. 576
Author(s):  
Budi Setiawan ◽  
Marwa Ben Abdallah ◽  
Maria Fekete-Farkas ◽  
Robert Jeyakumar Nathan ◽  
Zoltan Zeman
Keyword(s):  
Financial Crisis ◽  
Stock Market ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Market Returns ◽  
Stock Market Returns ◽  
The Global Financial Crisis ◽  
The Impact

COVID-19 pandemic has led to uncertainties in the financial markets around the globe. The pandemic has caused volatilities in the financial market at varying magnitudes, in the emerging versus developed economy. To examine this phenomenon, this study investigates the impact of COVID-19 pandemic on stock market returns and volatility in an emerging economy, i.e., Indonesia, versus developed country, i.e., Hungary, using an event-study approach methodology utilizing GARCH (1,1) model. In this study, the Jakarta Composite Index (JCI) and the b (BUX) data were obtained from Investing and Bloomberg, covering two global events observed within the selected period from 27 September 2006 to 31 August 2021. The data is compared with the stock market volatility data from the global financial crisis in 2007/08. Findings reveal that the recent COVID-19 pandemic had negative stock market returns at a greater magnitude compared to the global financial crisis, in both the emerging and developed economy’s equity market. Stock markets in Indonesia and Hungary have experienced volatility during the crisis. While comparing the result between COVID-19 and the global financial crisis, we found that the volatility on the stock markets is higher in the COVID-19 pandemic than during the global financial crisis. The higher stock market negative returns and volatility during the COVID-19 pandemic triggered the lockdown and limited economic activities, which impacted supply and demand shock. The virus’s propagation and mutation are continually evolving, reminding us that the pandemic is far from over. Developed countries with larger fiscal space seem to find it easier to make responsive policies than countries with a tighter financial budget. Fiscal and monetary policies seem to be a quick solution to stabilize the economy and maintain investor confidence in the Indonesian and Hungarian capital markets. Furthermore, the extension of stock market volatility understanding ensures relevant information for investors, which benefits to mitigate the risk and build sustainable investments of the unprecedented events and enables the promotion of Sustainable Development Goal number 8 (SDG8) to communities, with access to financial products including the stock market, especially during economic and financial uncertainties.

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.

scholarly journals Financial contagion: The impact of the volatility of global stock exchanges on the Lima-Peru Stock Exchange

2020 ◽  
Vol 1 (1) ◽  
pp. 13-27
Author(s):  
Pedro Pablo Chambi Condori
Keyword(s):  
Stock Market ◽  
Financial Markets ◽  
Stock Exchange ◽  
Market Volatility ◽  
Financial Contagion ◽  
Stock Market Volatility ◽  
Important Indicator ◽  
Dynamic Correlation ◽  
High Volatility ◽  
The Impact

What happens in the international financial markets in terms of volatility, have an impact on the results of the local stock market financial markets, as a result of the spread and transmission of larger stock market volatility to smaller markets such as the Peruvian, assertion that goes in accordance with the results obtained in the study in reference. The statistical evaluation of econometric models, suggest that the model obtained can be used for forecasting volatility expected in the very short term, very important estimates for agents involved, because these models can contribute to properly align the attitude to be adopted in certain circumstances of high volatility, for example in the input, output, refuge or permanence in the markets and also in the selection of best steps and in the structuring of the portfolio of investment with equity and additionally you can view through the correlation on which markets is can or not act and consequently the best results of profitability in the equity markets. This work comprises four well-defined sections; a brief history of the financial volatility of the last 15 years, a tight summary of the background and a dense summary of the methodology used in the process of the study, exposure of the results obtained and the declaration of the main conclusions which led us mention research, which allows writing, evidence of transmission and spread of the larger stock markets toward the Peruvian stock market volatility, as in the case of the American market to the market Peruvian stock market with the coefficient of dynamic correlation of 0.32, followed by the Spanish market and the market of China. Additionally, the coefficient of interrelation found by means of the model dcc mgarch is a very important indicator in the structure of portfolios of investment with instruments that they quote on the financial global markets.

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