Does Google search index really help predicting stock market volatility? Evidence from a modified mixed data sampling model on volatility

2019 ◽  
Vol 166 ◽  
pp. 170-185 ◽  
Author(s):  
Qifa Xu ◽  
Zhongpu Bo ◽  
Cuixia Jiang ◽  
Yezheng Liu
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Mixed Data ◽  
Stock Market Volatility ◽  
Data Sampling ◽  
Mixed Data Sampling ◽  
Search Index ◽  
Google Search

China’s Macroeconomic Fundamentals on Stock Market Volatility: Evidence from Shanghai and Hong Kong

2017 ◽  
Vol 20 (02) ◽  
pp. 1750014 ◽  
Author(s):  
Andy Wui Wing Cheng ◽  
Iris Wing Han Yip
Keyword(s):  
Hong Kong ◽  
Price Index ◽  
Consumer Price Index ◽  
Market Volatility ◽  
Mixed Data ◽  
Stock Market Volatility ◽  
Data Sampling ◽  
Producer Price ◽  
Macroeconomic Variables ◽  
Mixed Data Sampling

This paper examines the effect of Chinese macroeconomic variables, the industrial production growth rate, the producer price index, the 3-month short-term Shanghai Interbank Offer Rate and the consumer price index, on the volatility of the Shanghai and Hong Kong stock markets. We apply the generalized autoregressive conditional heteroskedastic mixed data sampling model for the study. Our empirical findings on various indexes and enterprises in the Shanghai and Hong Kong markets show that Chinese macroeconomic variables have a greater power to explain the volatility in Hong Kong than in Shanghai. They also contribute significantly to Hong Kong’s market volatility.

Estimating Emerging Stock Market Volatility Using Garch Family Models

2011 ◽  
Vol 3 (9) ◽  
pp. 331-333 ◽  
Author(s):  
Ramona Birău ◽  
◽  
Jatin Trivedi
Keyword(s):  
Stock Market ◽  
Market Volatility ◽  
Stock Market Volatility ◽  
Family Models

Measuring the Information Content of the Beige Book: A Mixed Data Sampling Approach

2008 ◽  
Author(s):  
Michelle T. Armesto ◽  
Ruben Hernandez-Murillo ◽  
Michael Owyang ◽  
Jeremy M. Piger
Keyword(s):  
Information Content ◽  
Mixed Data ◽  
Data Sampling ◽  
Mixed Data Sampling ◽  
Sampling Approach

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.

scholarly journals COVID-19 Outbreak and CO2 Emissions: Macro-Financial Linkages

2020 ◽  
Vol 14 (1) ◽  
pp. 12
Author(s):  
Julien Chevallier
Keyword(s):  
Co2 Emissions ◽  
Fossil Fuels ◽  
Mixed Data ◽  
Energy Information Administration ◽  
Time Varying ◽  
Data Sampling ◽  
Quantitative Finance ◽  
Financial Variables ◽  
Mixed Data Sampling ◽  
The One

In the Dynamic Conditional Correlation with Mixed Data Sampling (DCC-MIDAS) framework, we scrutinize the correlations between the macro-financial environment and CO2 emissions in the aftermath of the COVID-19 diffusion. The main original idea is that the economy’s lock-down will alleviate part of the greenhouse gases’ burden that human activity induces on the environment. We capture the time-varying correlations between U.S. COVID-19 confirmed cases, deaths, and recovered cases that were recorded by the Johns Hopkins Coronavirus Center, on the one hand; U.S. Total Industrial Production Index and Total Fossil Fuels CO2 emissions from the U.S. Energy Information Administration on the other hand. High-frequency data for U.S. stock markets are included with five-minute realized volatility from the Oxford-Man Institute of Quantitative Finance. The DCC-MIDAS approach indicates that COVID-19 confirmed cases and deaths negatively influence the macro-financial variables and CO2 emissions. We quantify the time-varying correlations of CO2 emissions with either COVID-19 confirmed cases or COVID-19 deaths to sharply decrease by −15% to −30%. The main takeaway is that we track correlations and reveal a recessionary outlook against the background of the pandemic.

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