Highlights EMV-ID tracker is used to measure the infectious disease pandemic. GARCH-MIDAS is adopted to model the impacts of EMV-ID on stock market volatility. Lagged realized volatility and economic policy uncertainty are used as controlling variables. Infectious disease pandemic imposes significant positive impact on stock market volatility. Infectious disease pandemic has the smallest impact on permanent volatility of china's stock market.

Effectively explaining and accurately forecasting industrial stock volatility can provide crucial references to develop investment strategies, prevent market risk and maintain the smooth running of national economy. This paper aims to discuss the roles of industry-level indicators in industrial stock volatility. Selecting Chinese manufacturing purchasing managers index (PMI) and its five component PMI as the proxies of industry-level indicators, we analyze the contributions of PMI on industrial stock volatility and further compare the volatility forecasting performances of PMI, macroeconomic fundamentals and economic policy uncertainty (EPU), by constructing the individual and combination GARCH-MIDAS models. The empirical results manifest that, first, most of the PMI has significant negative effects on industrial stock volatility. Second, PMI which focuses on the industrial sector itself is more helpful to forecast industrial stock volatility compared with the commonly used macroeconomic fundamentals and economic policy uncertainty. Finally, the combination GARCH-MIDAS approaches based on DMA technique demonstrate more excellent predictive abilities than the individual GARCH-MIDAS models. Our major conclusions are robust through various robustness checks.

In this paper, we explore the dynamics of the return connectedness among major commodity assets (crude oil, gold and corn) and financial assets (stock, bond and currency) in China and the US during recent COVID-19 pandemic by using the time-varying connectedness measurement introduced by Antonakakis et al. (2020). Firstly, we find that the total return connectedness of the US commodity and financial assets is stronger than that of the Chinese commodity and financial assets in most cases, and both of them increase rapidly after the outbreak of COVID-19. Secondly, gold is a net transmitter of return shocks in both the Chinese and the US markets before the burst of COVID-19 pandemic, while stock and currency become net transmitters of shocks in both markets after that. Thirdly, corn usually receives the shocks from other commodity and financial assets in both China and the US markets during the COVID-19 epidemic, and the shocks it receives peak during this period, making it the strongest net receiver of shocks. Fourthly, crude oil shifts from a net transmitter to a net receiver of shocks in China after the outbreak of COVID-19, but it remains to be a net transmitter of shocks in the US. Finally, bond changes from a net receiver to a net transmitter of shocks in China after the outbreak of the epidemic, but converts from a net transmitter to a net receiver of shock in the US. The interchangeable roles of the commodity and financial assets suggest flexible regulatory and portfolio allocation strategies should be applied by policy makers and investors.

No abstract

In this paper, we detect the contributions of various predictors in terms of density forecasts of monthly West Texas Intermediate (WTI) crude oil prices. In the first step, we use a simple predictive regression of crude oil prices on different predictors one-by-one as explanatory variables, and then two kinds of criteria, Log Score and Continuous Ranked Probability Score (CRPS), are employed to evaluate the density forecasting accuracy of them. In the second step, we utilize a CRPS-weighted combination and an equal-weighted combination, respectively, to assemble various density forecasting results in the first step. Finally, a novel test proposed by Rossi and Sekhposyan ( 2019) is adopted to verify the correct calibration of predictive densities by these two combination methods as well as an AR benchmark model. The empirical results indicate that those predictors proved to perform well (poor) in point forecasts of crude oil price in extant literature do not necessarily offer high (low) density forecasting accuracy. Interestingly, WTI oil futures price is the only predictor that can produce good out-of-sample density forecasts across various time horizons. In addition, we find that the two model combination methods can beat the AR benchmark in density forecasting of crude oil price. However, no model can produce correct calibration of predictive densities for crude oil price at time horizons longer than about 5 years.