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

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.

On the Relationship between Oil and Exchange Rates of Oil-Exporting and Oil-Importing Countries: From the Great Recession Period to the COVID-19 Era

This paper is dedicated to studying and modeling the interdependence between the oil returns and exchange-rate movements of oil-exporting and oil-importing countries. Globally, twelve countries/regions are investigated, representing more than 60% and 67% of all oil exports and imports. The sample period encompasses economic and natural events like the Great Recession period (2007–2009) and the COVID-19 pandemic. We use the dynamic conditional correlation mixed-data sampling (DCC-MIDAS) model, with the aim of investigating the interdependencies expressed by the long-run correlation, which is a smoother (but always daily observed) version of the (daily) time-varying correlation. Focusing on the advent of the COVID-19 pandemic in 2020, the long-run correlations of the oil-exporting countries (Saudia Arabia, Russia, Iraq, Canada, United States, United Arab Emirates, and Nigeria) and (lagged) WTI crude oil returns strongly increase. For a subset of these countries (that is, Saudia Arabia, Iraq, United States, United Arab Emirates, and Nigeria), the (lagged) correlations turn out to be positive, while for Canada and Russia they remain negative as before the advent of the pandemic. In addition, the oil-importing countries and regions under investigation (Europe, China, India, Japan, and South Korea) experience a similar pattern: before the COVID-19 pandemic, the (lagged) correlations were negative for China, India, and South Korea. After the COVID-19 pandemic, the correlations of these latter countries increased.

On the Predictability of China Macro Indicator with Carbon Emissions Trading

Accurate and timely macro forecasting requires new and powerful predictors. Carbon emissions data with high trading frequency and short releasing lag could play such a role under the framework of mixed data sampling regression techniques. This paper explores the China case in this regard. We find that our multiple autoregressive distributed lag model with mixed data sampling method setup outperforms either the auto-regressive or autoregressive distributed lag benchmark in both in-sample and out-of-sample nowcasting for not only the monthly changes of the purchasing managers’ index in China but also the Chinese quarterly GDP growth. Moreover, it is demonstrated that such capability operates better in nowcasting than h-step ahead forecasting, and remains prominent even after we account for commonly-used macroeconomic predictive factors. The underlying mechanism lies in the critical connection between the demand for carbon emission in excess of the expected quota and the production expansion decision of manufacturers.

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

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.

Asymmetric Responses of Economic Growth to Daily Oil Price Changes: New Global Evidence from Mixed-data Sampling Approach

Our paper inspects empirically the asymmetric impact of daily oil price shocks on the quarterly real domestic product in eight countries during the period (1983–2016). We employ two methodologies Ordinary Least Squares (OLS) and Asymmetric Mixed Data Sampling (AMIDAS). The OLS technique shows that the positive oil price shocks have a statistically significant negative effect on economic growth in all the countries and vice versa. In addition, it reveals that this relationship could be either symmetric or asymmetric in all the countries. On the contrary, the AMIDAS gives more important details and proves that all the relationships in our sample data are asymmetric. Thus, we think that the AMIDAS technique leads to more accurate results which enhances a better insightful of an energy policy. The policy implication of our paper demonstrates that the energy policies are significant procedures to improve economic performance.

REAL-TIME PROBABILISTIC NOWCASTS OF UK QUARTERLY GDP GROWTH USING A MIXED-FREQUENCY BOTTOM-UP APPROACH

We propose a nowcasting system to obtain real-time predictive intervals for the first-release of UK quarterly GDP growth that can be implemented in a menu-driven econometric software. We design a bottom-up approach: forecasts for GDP components (from the output and the expenditure approaches) are inputs into the computation of probabilistic forecasts for GDP growth. For each GDP component considered, mixed-data-sampling regressions are applied to extract predictive content from monthly and quarterly indicators. We find that predictions from the nowcasting system are accurate, in particular when nowcasts are computed using monthly indicators 30 days before the GDP release. The system is also able to provide well-calibrated predictive intervals.