Shawkat M Hammoudeh

This study explores the effects of education and export diversification on carbon emissions in India and China covering the annual data from 1971 to 2017. While modelling the education and export with emissions, we included two additional variables, namely energy consumption and economic growth, in the model. In order to take care of single structural break in the data series, we employed the unit root test of Kim and Perron (2009). Further, to estimate the model, we applied the recently developed bootstrapping autoregressive-distributed lag (B-ARDL) approach of McNown et al. (2018). The empirical analysis depicts that the variables are moving together in the long run. Economic growth increases and then decreases carbon emissions in both China and India. In China, education is positively linked with environmental quality, but in India it aggravates environmental deterioration. Energy consumption showed a directly significant impact on carbon dioxide (CO2) emissions for both economies. Our findings validate the environmental Kuznets curve (EKC) in context of both China and India. The association between education and carbon emissions is an inverted U shape in China. Moreover, the inverted U-shaped association between exports diversification and carbon emissions is also validated for the Indian economy, but it is U-shaped for the Chinese economy.

This study examines migration and cascading of credit default swaps (CDS) risks among four oil-related sectors -autos, chemical, oil and natural gas production, and utility—in two models. Model 1 encompasses fundamental variables, and Model 2 includes market risks. The key finding of the study suggests that replacing the two financial fundamental variables (the 10-year Treasury bond rate and the S&P 500 index) of Model 1 with the two market risk variables (the S&P VIX and the Oil VIX) of Model 2 reduce the long- and short-run risk migration and cascading in the second model for both the full sample and the subperiod. The CDS and VIX indices both reflect fear and risk on their own. Among the four oil-related CDS spreads, the chemical and auto spreads are the most responsive to the other credit and market risks and the fundamentals in the long-run, while those of utility and oil and natural gas sectors are not responsive. The recent quantitative easing in the United States adds to spikes in the levels of the chemical CDS and the S&P 500 index in Model 1, and to the S&P VIX and default risk spread in Model 2. Implications for model builders and policy makers are also discussed.