This study examines whether the dynamic relationship between the Chinese and international fossil markets changed during the 2008 financial crisis and is changing during the COVID-19 pandemic. The impact of the crises are analyzed by including the periods affected by the crises as dummy variables in the VAR and VECM models. Monthly data for the 2000:1–2020:12 period were used in the study. Our results suggest that the effects of the COVID-19 on the linkages between the Chinese and international fossil fuel markets are not as evident compared to the 2008 financial crisis. The study identifies that the effects of the 2008 financial crisis and the COVID-19 pandemic on the linkages are mostly driven by the impacts of these crises on the Chinese fossil fuel markets. The study indicates the importance of controlling the risk involved in the Chinese fossil fuel market when events like the 2008 financial crisis and the COVID-19 pandemic are changing the linkages between the Chinese and international fossil fuel markets.

This study examined how the relationships among the fossil fuel, clean energy stock, gold, and Bitcoin markets have changed since the COVID-19 pandemic took place for hedging the price change risks in the fossil fuel markets. We applied the Bayesian Dynamic Conditional Correlation-Multivariate GARCH (DCC-MGARCH) models using US daily data from 2 January 2019 to 26 February 2021. Our results suggest that the fossil fuel (WTI crude oil and natural gas) and financial markets (clean energy stock, gold, and Bitcoin) generally had negative relationships in 2019 before the pandemic prevailed, but they became positive for a while in mid-2020, alternating between positive (0.8) and negative values (−0.8). As it is known that negative relationships are required among assets to hedge the risk of price changes, this implies that stakeholders need to be cautious in hedging the risk across the fossil fuel and financial markets when a crisis like COVID-19 occurs. However, our study also revealed that such negative relationships only lasted for three to six months, suggesting that the effects of the pandemic were short term and that stakeholders in the fossil fuel markets could cross hedge with the financial markets in the long term.

This study employs mainly the Bayesian DCC-MGARCH model and frequency connectedness methods to respectively examine the dynamic correlation and volatility spillover among the green bond, clean energy, and fossil fuel markets using daily data from 30 June 2014 to 18 October 2021. Three findings arose from our results: First, the green bond market has a weak negative correlation with the fossil fuel (WTI oil, Brent oil, natural gas, heating oil, and gasoline) and clean energy markets, which means that green bonds play a critical hedging role against fossil fuel and clean energy. Second, the green bond and clean energy are net volatility receivers from WTI crude oil and heating oil for the short term, indicating that investors and policymakers need to pay attention to the WTI oil volatility spillover risk when promoting green bonds and clean energy. Third, the correlation and volatility spillover from WTI crude oil to green bonds and clean energy is stronger than that of Brent oil, which implies that investors and policymakers need to consider the price movements of WTI crude oil more than Brent oil when investing in the green bond market. In summary, our conclusion is that investors should be aware that green bond investing addresses the two-pronged investment strategy of (i) risk diversification and (ii) carbon mitigation. Thus, this study can provide essential information for energy investors and policymakers to achieve sustainable investment.

The Chinese liquid natural gas (LNG) import price has been unstable because the stability of LNG import prices is related to changes in the exchange rates. This paper analyzes the pass-through rate of the Chinese Yuan (CNY) and Japanese Yen (JPY) on the Chinese LNG import price. The Time-Varying Parameter vector autoregressive (TVP-VAR) model is adopted to verify the pass-through rate of the exchange rates on the LNG import price using the Markov chain Monte Carlo (MCMC) method. Since September 2005, the JPY pass-through rate on the Chinese LNG import price has been decreasing while that of the CNY has been increasing. Notably, the pass-through rate of CNY began to exceed that of JPY after 2008. Moreover, since 2005, the lag effect of the CNY on the Chinese LNG import price became longer compared to JPY. If any new currency reform of the CNY is implemented in the future, then the impact of JPY on the Chinese LNG import price could be reduced and the lag effect of the CNY on the Chinese LNG import price could become longer. Therefore, the fluctuation of the CNY is becoming an important factor in understanding the movements of the Chinese LNG import price. This implies the significance of considering the effect of the exchange rate on an energy market when the market is influenced by a monetary reform of the importing country.