Do climate policy uncertainty and investor sentiment drive the dynamic spillovers among green finance markets?

Green finance is an essential instrument for improving the environment and addressing climate change. This study investigates the dynamic spillovers among green finance markets using time-varying parameter vector autoregression (TVP-VAR) spillover indices, and further investigates the impact of climate policy uncertainty and investor sentiment on spillovers based on the generalised autoregressive conditional heteroscedasticity mixed data sampling (GARCH-MIDAS) model. The results indicate that: (i) environmental, social and governance (ESG), clean energy and water markets are information transmitters in the green finance system, whereas green building, green transportation, green bond and carbon markets are mainly information receivers; (ii) green stock markets including clean energy, non-energy and ESG markets transmit and receive greater information in the green finance system, while green bond and carbon markets do less; (iii) the green bond market is more interconnected with other green finance markets after the COVID-19 outbreak; (iv) investor sentiment contributes more to the net total directional spillovers of green resource markets (water and clean energy), while climate policy uncertainty contributes more to total spillovers and the net total directional spillovers of other green finance markets. These findings offer invaluable guidance for both policymakers and environmental investors.

The influence of COVID-19 epidemic on BDI volatility: An evidence from GARCH-MIDAS model.

In this study, we use the sample data from Jan 22, 2020 to Jan 21, 2022 to investigate the impacts of added infection number on the volatility of BDI. Under this structure, the control variables (freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls) are added to test whether the information contained in the added infection number is covered. In the GARCH-MIDAS model, we divide the volatility of BDI into the long-term and short-term components, then employ in the least squares regression to empirically test the influences of added infection number on the volatility. From the analysis, we find the added infection numbers effectively impact the BDI volatility. In addition, whether the freight rate, Brent crude oil price, container idle rate, port congestion level, global port calls and other variables are considered alone or at the same time, further the added infection number still significantly influences the volatility of BDI. By studying the ability of the confirmed number to explain the volatility of BDI, a new insight is provided for the trend prediction of BDI that the shipping industry can take the epidemic development of various countries as a reference to achieve the purpose of cost or risk control.

Infectious disease pandemic and permanent volatility of international stock markets: A long-term perspective.

Understanding the impact of infectious disease pandemic on stock market volatility is of great concerns for investors and policy makers, especially during recent new coronavirus spreading period. Using an extended GARCH-MIDAS model and a newly developed Infectious Disease Equity Market Volatility Tracker (EMV-ID), we investigate the effects of infectious disease pandemic on volatility of US, China, UK and Japan stock markets through January 2005 to April 2020. The empirical results show that, up to 24-month lag, infectious disease pandemic has significant positive impacts on the permanent volatility of international stock markets, even after controlling the influences of past realized volatility, global economic policy uncertainty and the volatility leverage effect. At different lags of eruptions in infectious disease pandemic, EMV-ID has distinct effects on various stock markets while it has the smallest impact on permanent volatility of China's stock market.

Predicting volatility in natural gas under a cloud of uncertainties.

The COVID-19 pandemic has triggered an economic crisis and the ensuing global uncertainty. The current Russian-Ukrainian conflict has escalated tensions in various regions and increased various uncertainties in the financial and economic system. These uncertainties have had a significant impact on the development of the natural gas market during the current critical period of carbon neutrality and energy transition. This paper explores the impact of various uncertainties on price volatility in the U.S. natural gas futures market using the GARCH-MIDAS model. We considered eleven types of uncertainties, including four US economic policy uncertainties, four global uncertainty indicators, and oil supply-demand uncertainty closely related to the natural gas market. The in-sample empirical results find that various uncertainties can impact the natural gas market. However, through out-of-sample testing, we find that economic policy uncertainty has more predictive power than other indicators in predicting natural gas price fluctuations. Interestingly, oil supply-demand uncertainty surpasses global indicators and can provide forecasting information for natural gas markets. Therefore, in the current context of high uncertainty, our research may offer better decision-making opinions for market participants.

The return volatility of cryptocurrencies during the COVID-19 pandemic: Assessing the news effect.

In this paper, we test the role of news in the predictability of return volatility of digital currency market during the COVID-19 pandemic. We use hourly data for cryptocurrencies and daily data for the news indicator, thus, the GARCH MIDAS framework which allows for mixed data frequencies is adopted. We validate the presupposition that fear-induced news triggered by the COVID-19 pandemic increases the return volatilities of the cryptocurrencies compared with the period before the pandemic. We also establish that the predictive model that incorporates the news effects forecasts the return volatility better than the benchmark (historical average)model.