In the increasingly frequent global financial turmoil, investors prefer to invest in stable assets to hedge risks. Crude oil naturally has dual use value as a general commodity and as a financial asset, which has attracted wide attention. In this paper, we adopt a wavelet coherence analysis to study the standard of crude oil as a hedging asset and analyze the dynamic correlation of crude oil and stock market price fluctuations in the four economies of the United States, Japan, China and Hong Kong at different frequencies. The empirical evidence shows that crude oil can be conditionally used as a hedging asset for underlying securities. From the perspective of space, crude oil is suitable for investors in China’s stock market as a hedging asset, while for stock markets in the US, Japan and Hong Kong, the ability of crude oil to hedge risk has been greatly weakened. From the perspective of investment term, although crude oil cannot be regarded as a hedging asset for long-term investment, it can still play a hedging role in the short term. When the market is in a state of panic, the ability of oil to hedge risk is stronger.

Due to the crucial implication of oil risks for economic growth and policy making, the aim of this paper is to explore the heterogeneous interconnections of supply or demand in oil risks over time horizons and different countries. Specifically, we first examine the correlation of supply or demand in oil return risks and show the relationships in different countries based on wavelet coherence. Furthermore, we explore the time-varying interconnections between supply- or demand-side and oil return risks, as well as oil producers and demand countries. The empirical results show that the correlation between supply and oil return risks is relatively stable, whereas the linkage between demand and oil return risks shows greater volatility due to the impact of specific events. Further study indicates that there are heterogeneous interconnections between supply- or demand-side and oil return risks over sample periods. Specifically, the sign of response could be divided into four phases, i.e., 1997–2002, 2002–2010, 2010–2013 and 2014–2018. In addition, the interconnections of the demand side could be divided into three phases due to the sign of it. What is more, the dynamic interconnections of oil producers’ or countries’ demands behave quite heterogeneously in different countries. Thus policymakers should focus on the coordination level and space capacity in the global crude oil market.

This study is to investigate the heterogeneous effect of economic policy uncertainty (EPU) on the exchange rate volatility of China using quantile regression for the period Jan 2003–Jan 2019. This paper significantly contributes to the empirical literature by taking into account the effect in individual and distributional heterogeneity and exploring the transmission mechanism of heterogeneity. The results demonstrate that, first, EPU from different countries have a heterogeneous impact on China's exchange rate volatility both direction as well as distribution. Second, this paper further assesses the EPU transmission mechanism of bilateral trading and interest rate difference is mixed. These results provide policymakers with critical policy recommendations that contribute to the reduction of the exchange rate volatility and ensure stable economic development in China.

The channels of monetary policy transmission highly depend on industrial structure, and there are significant differences across industries. This paper studies the industrial effects of monetary policy in China using the Vector Autoregression (VAR) model. We explore the heterogeneity of the response three pillar industries and factor intensity industries to a monetary shock. In order to capture the transmission of monetary policy, decomposing monetary policy as anticipated and unanticipated is considered. Results show evidence of the industrial effects of aggregate monetary policy shock is almost not effective. And the effectiveness of monetary policy shocks is conducted in close association with expectations and factor intensity.

This paper identifies a structural break in exchange rate returns from July 2005 to May 2020 using the iterated cumulative sums of squares (ICSS) algorithm. Then we explore the changing effect of determinants on exchange rates based on the time-varying parameter structural Vector AutoRegression (TVP-SVAR) model. Empirically, we find a single break based on exchange rate returns and caused by regime reform. Further analysis suggests a significant change in the shocks to inflation, foreign exchange reserves, oil prices, and short-term interest rates. In addition, the domestic and international determinants of exchange rates are heterogeneous over different periods in the short and long run. In the short run, the relation between exchange rates and inflation, the interest rate, foreign exchange reserves, and oil price, is significantly negative before August 2015. However, since August 2015, the relationship between inflation and exchange rates in China has been positive. Only short-term interest rates are affected by structural changes. This study can guide policy makers in formulating the appropriate exchange rate regime to attain sustainable market efficiency over the long run.