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This study examines the interest rate pass-through (IRPT) mechanism in China after the interest rate liberalization by using the nonlinear ARDL (NARDL) model proposed by Shin et al. (2014). In order to dissect the IRPT of China appropriately, the interest rate transmission is divided into three stages in which changes in the policy rate are first transmitted to the target market rate and in turn, to the other interbank market rates and treasury bond rates, and eventually to the bank retail rates and corporate bond rates. The empirical results show that though changes in the monetary policy rate can be completely transmitted to the policy target market rate, they appear to be passed on to the other interbank market rates excessively and to the bank lending rates incompletely. Moreover, the pass-through from the policy target rate to the bond market rates is in general more sufficient than the pass-through to the bank retail rates. In addition, the patterns of asymmetry can be identified for the pass-through in the second and third stages, with the impact of positive shocks on the policy target rate being more pronounced than that of negative shocks either in the short or long run. These findings suggest that the interest rate transmission after the interest rate liberalization in China is far from being as effective as expected, and policy rate cuts might not have desirable effects on the real economy in the context of the recession.

This study examines the dynamic volatility connectedness between different types of policy uncertainty and sectoral stock markets in both the time and frequency domains in China. Using the time–frequency connectedness index approach, we find extremely high connectedness between policy uncertainty and Chinese sectoral stock markets mainly in the medium and long terms. In particular, uncertainty regarding monetary policies makes the weakest contribution to spillovers among the four policy uncertainties, while uncertainty regarding trade policies contributes prominently. The energy, financial, IT, telecommunication services and utilities sectors are the most vulnerable to policy uncertainties.

This study aims to explore the comovement and causal relationship between the stock market cycle and business cycle in China over the period from 1992Q1 to 2018Q1. While the existing literature treats the relationship as being time- and/or frequency-invariant, we attempt to detect possible time- and frequency-varying patterns by using wavelet analysis. The results show that the stock market cycle tends to lead the business cycle in expansion periods whereas the business cycle tends to lead the stock market in recession periods. Moreover, when the stock market cycle leads the business cycle, they are always positively correlated. In contrast, when the business cycle leads the stock market cycle, they tend to be negatively correlated. In addition, we find that there are substantial time- and frequency-variations in the comovement and causal relationship between the two cycles in China, suggesting the presence of the time- and frequency-variation features should not be ignored in future research. Finally, the relationship can be significantly affected by the domestic interest rates as well as the effect of some external shocks, such as the interest rate and business cycle shocks originated from other major advanced countries. These findings shed new light on the literature that merely considers one specific subject of the relationship and ignores the time and frequency-varying natures in the relationship.

This paper adopts wavelet analysis to explore the time–frequency comovement and causality between the exchange rates and the interest rate differentials in China (compared to the USA) over the period from February 1999 to March 2018. While the existing literature in general treats the relationship between the two variables as being symmetric, this paper attempts to detect possible asymmetric patterns by extending the standard wavelet analysis to an asymmetric analysis. In addition, given that the relationship might be affected by exchange rate expectations and foreign exchange interventions, we further employ the partial wavelet tools to filter out the effects of the two controlled variables. The results show that the general pattern of the comovement and causality does not change. The comovement is intensified at the low frequency after the exchange rate reform in 2005. Moreover, exchange rates are found to positively comove with interest rate differentials, and the former leads the latter. More importantly, we provide robust evidence of meaningful asymmetry and substantial time and frequency variations in the comovement and causality between the two variables. These findings provide important implications for improving the transmission of monetary policy in China.

This study investigates whether bubbles exist in Bitcoin markets and tracks when they will occur and collapse. We apply the generalized sup Augmented Dickey-Fuller test method proposed by Phillips et al. (2013). The results show that there have been four explosive bubbles in China and the U.S. market, primarily occurring during the periods of huge surges in Bitcoin prices. This is consistent with the bubble model improved by Gurkaynak (2008), in which asset price is decomposed into fundamental and bubble components. In particular, exogenous shocks, including foreign or domestic economic events, lead to the origination of bubbles. A serious financial crisis may trigger long-term and large-scale bubbles, whereas relatively short-term bubbles are caused by domestic components. It can be inferred that Bitcoin can be used as a hedge against market-specific risk. Finally, Bitcoin bubbles collapse due to administrative intervention by economic authorities. Therefore, the government should manage public expectations to maintain confidence in authority and reduce speculation behavior to stabilize the asset price and financial market.

Detecting the adverse effects of major emergencies on financial markets and real economy is of great importance not only for short-term policy reactions but also for economic and financial stability. This is the lesson we learnt from the COVID-19 pandemic. This paper focuses on the risk spillover effect of the COVID-19 on Chinese energy industry using a high-dimensional and time-varying factor-augmented VAR model. The results show that the net volatility spillovers of the pandemic remain positive to all underlying energy sectors during January to June of 2020 and February to April of 2021. For the former sub-period, the volatility spillover of the COVID-19 is not only the highest, but also lasts longest for oil exploitation sector, followed by the power and gas sectors. While for the latter sub-period, the COVID-19 has relatively higher volatility spillovers to the power, coal mining and petrochemical sectors. These findings suggest that the COVID-19 has significant risk spillover effects on Chinese energy sectors, and the effects vary among different energy sub-sectors and across different periods of time.

This paper analyzes the causal relationship between trade policy uncertainty, government subsidies, and the role of political connections using annual data on Chinese energy firms from 2003 to 2018. The results show that when trade policy uncertainty increases, the Chinese government tends to increase subsidies for energy firms, and firms with political ties may receive more subsidies. Furthermore, firms with poor sales performance and in less marketized regions will be granted even more subsidies when uncertainty rises and firms are politically connected. We also observe that subsidies promote firms' fixed asset investments and innovations but decrease their overall investment efficiency.

This paper investigates the effects of financial deregulation on the energy enterprises' operational risks in China, based on 230 Chinese energy enterprises' yearly data spanning from 2003 to 2018. The results show that financial deregulation can lower the energy enterprise's operational risks through easing financing constraints and reversing the financialization tendency of energy enterprises. In addition, the impact of financial deregulation is heterogeneous across different features of energy enterprises. After replacing core indicators, changing model settings, and solving endogenous problems, the above conclusions are still robust, which provides robust evidence from the perspective of the interest rates liberalization.

This study utilizes the nonlinear ARDL (NARDL) model proposed by Shin, Yu, and Greenwood-Nimmo (2014) to quantify the potentially asymmetric transmission of positive and negative changes in each of the possible determinants of industry-level corporate bond credit spreads in China. The determinants we consider include the corresponding industry stock price, China’s stock market volatility, the level and slope of the yield curve (i.e., the interest rate), the industrial production growth rate, and the inflation rate. The empirical results suggest substantial asymmetric effects of these determinants on credit spreads, with the positive changes in the determinants showing larger impacts than the negative changes for most industries we consider. Moreover, the corresponding industry stock prices, the interest rate, and the industrial production growth rate negatively drive the industry credit spreads for many industries. In turn, China’s stock market volatility and the inflation rate positively affect the credit spreads at each industry level. These findings may be helpful to investors, bond issuers and policymakers in understanding the dynamics of credit risks and corporate bond rates at the industry level.

This study investigates the asymmetric effects of economic policy uncertainty (EPU) and other possible determinants on the CNY–CNH spreads by utilizing the nonlinear ARDL model. For this purpose, we construct a novel EPU index based on the EPU indices of China and the G7 countries by using principal component analysis. The results show substantial asymmetric effects of the concerned determinants on the spreads. Moreover, the composite EPU significantly affects the spreads, with positive shocks to the composite EPU inducing widening CNY–CNH spreads.

Spatially autoregressive varying coefficient models are a powerful tool for simultaneously dealing with spatial dependence and spatial heterogeneity in spatial data analysis. Different methods have been developed for estimating the models. Nevertheless, little work has been devoted to their statistical inference issues. In this paper, two generalized-likelihood-ratio-statistic-based bootstrap tests are developed to detect spatial autocorrelation in the response variable and to identify constant coefficients in the regression functions, respectively. The simulation studies show that both tests are of accurate size and satisfactory power. The Boston house price data are finally analyzed to demonstrate the application of the proposed tests in the detection of spatial dependence and heterogeneity.