scholarly journals A Transmission of Beta Herding during Subprime Crisis in Taiwan’s Market: DCC-MIDAS Approach

2021 ◽  
Vol 9 (4) ◽  
pp. 70
Author(s):  
Yi-Chang Chen ◽  
Hung-Che Wu ◽  
Yuanyuan Zhang ◽  
Shih-Ming Kuo
Keyword(s):  
Mixed Data ◽  
Subprime Crisis ◽  
Time Varying ◽  
Data Sampling ◽  
Switching Regression ◽  
Conditional Correlation ◽  
Forecasting Method ◽  
Regression Approach ◽  
Mixed Data Sampling ◽  
Robustness Check

The aim of this study is to investigate the herding of beta transmission between return and volatility. We have used the dynamic conditional correlation model with the mixed-data sampling (DCC-MIDAS) model for the analysis. The evidence demonstrates that herding is a key transmitter in Taiwan’s stock market. The significant estimation of DCC-MIDAS explains that the herding phenomenon is highly dynamic and time-varying in herding behavior. By means of time-varying beta of herding based on our rolling forecasting method and robustness check of the Markov-switching regression approach using four types of portfolios, the evidence indicates that there are conditional correlations between betas and herding. In addition, it also reveals that herding forms in Taiwan’s markets during the subprime crisis period.

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.

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 Forecasting Tourism Demand with an Improved Mixed Data Sampling Model

2020 ◽  
Vol 60 (2) ◽  
pp. 336-353 ◽  
Author(s):  
Long Wen ◽  
Chang Liu ◽  
Haiyan Song ◽  
Han Liu
Keyword(s):  
Factor Model ◽  
Moving Average ◽  
Demand Forecasting ◽  
Mainland China ◽  
Mixed Data ◽  
Dynamic Factor ◽  
Data Sampling ◽  
Tourism Demand ◽  
Composite Indices ◽  
Mixed Data Sampling

Search query data reflect users’ intentions, preferences and interests. The interest in using such data to forecast tourism demand has increased in recent years. The mixed data sampling (MIDAS) method is often used in such forecasting, but is not effective when moving average (MA) dynamics are involved. To investigate the relevance of the MA components in MIDAS models to tourism demand forecasting, an improved MIDAS model that integrates MIDAS and the seasonal autoregressive integrated moving average process is proposed. Its performance is tested by forecasting monthly tourist arrivals in Hong Kong from mainland China with daily composite indices constructed from a large number of search queries using the generalized dynamic factor model. The forecasting results suggest that this new model significantly outperforms the benchmark model. In addition, comparing the forecasts and nowcasts shows that the latter generally outperforms the former.

scholarly journals Determinants of the Long-Term Correlation between Crude Oil and Stock Markets

Energies ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 4123 ◽  
Author(s):  
Lu Yang ◽  
Lei Yang ◽  
Kung-Cheng Ho ◽  
Shigeyuki Hamori
Keyword(s):  
Crude Oil ◽  
Stock Markets ◽  
Global Financial Crisis ◽  
Debt Crisis ◽  
Panel Data Analysis ◽  
Mixed Data ◽  
Data Sampling ◽  
Conditional Correlation ◽  
Macroeconomic Factors

This study employed a dynamic conditional correlation–mixed-data sampling (DCC–MIDAS) approach and panel data analysis to examine the factors that influence the long-term correlation between crude oil and stock markets. Our study shows that there is a positive long-term conditional correlation between oil prices and stock markets, except during the 2008 global financial crisis and the 2011 European debt crisis. We also found that macroeconomic factors have a significant impact on this correlation. Specifically, risk-free rate has a positive effect, whereas economic activity and credit risk has a negative effect. Our results provide useful information for investors and monetary authorities.

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