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The total factor productivity of grain (TFPG) is critically important to secure food production, while its spatiotemporal heterogeneity in the urbanized area is largely ignored. Selecting 41 cities in the Yangtze River Delta, this study uses the data envelopment analysis (DEA) Malmquist index method to measure the TFPG in each city from 2012 to 2020 based on panel data, and explores the driving factors of the spatiotemporal evolution of the TFPG with the geographically and temporally weighted regression model. The results indicate the following: (1) Both the TFPG and technological progress varies in the same direction, indicating that technological progress dominates the TFPG in the studied region. The changes in technical efficiency, pure technical efficiency, and scale efficiency are relatively stable. (2) The spatial distribution of the TFPG shows a decentralized trend, with a pattern of high in the north and east areas and low in the south and west areas. (3) The driving factors, such as the development level of the grain economy, the amount of fertilizer used per unit area, and gross domestic product (GDP) per capita, have a restraining effect on the improvement of the TFPG, in which the amount of fertilizer used per unit area is the critical factor. (4) The scale of per capita labor operation, the proportion of the grain-growing population, and output of grain per hectare exert a promoting effect on the TFPG, in which both the proportion of the grain-growing population and output of grain per hectare are the critical factors. Finally, improving the efficiency of fertilizer use, expanding the production scale of the grain planting industry, and increasing the output of grain per hectare are proposed to improve the TFGP in the Yangtze River Delta. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the spillover effects of financial stress among five important financial markets (bond, stock, foreign exchange, interbank, and real estate markets) in China, and explore the important determinants of financial stress spillover level among the markets and the impact of the Chinese stress spillover situation on the European markets. Our findings are as follows: First, there is a significant stress spillover effect among the five markets, and the total financial stress spillover index (TSSI) is very high during the global financial crisis. Generally, the stock and real estate markets are the major transmitters of stress spillover, and the interbank and bond markets are the major receivers. Second, the most macro factors have significant impacts on the financial stress spillover level among the markets, especially CPI index, the Chinese economic policy uncertainty index and VIX index. And the severity of the COVID-19 epidemic in China and the world has a significant impact on the TSSI, especially from March 2020 to August 2020. Finally, the TSSI can significantly increase the volatility of French stock market, Italian stock market and German government bond market, especially during the Sino-US trade war and the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

We explore the role of public climate attention, captured by the Baidu search volume index, in the downside risk. Using 45 keywords from five perspectives related to climate change, we construct a public climate attention index in China. We find a positive and significant relationship between climate attention and downside risk at the market‐level and firm‐level. Moreover, the risk‐increase effect of climate attention becomes more prominent for state‐owned and high‐carbon‐emission firms. Further analysis shows that excellent sustainable performance can moderate the adverse effect of rising climate attention, while the major climate disasters exacerbate the effect. Overall, our findings shed additional light on the important role of collective climate beliefs in corporate risk management and investor decision‐making. [ABSTRACT FROM AUTHOR]

We investigate how both traditional bank credit and shadow banking credit affect housing prices and financial stress based on China's monthly data from January 2003 to December 2019. Our results show that: (a) the effects of bank credit shocks and shadow banking credit shocks on housing prices partly offset each other; (b) increasing bank loans in recessions will increase financial instability, while increasing shadow bank credit in recessions will lower financial instability; (c) commercial bank credit plays a more important role in explaining housing price fluctuations, while shadow banking credit plays a more important role in explaining financial stress fluctuations. Our results are robust to a wide battery of checks and have important policy implications. [ABSTRACT FROM AUTHOR]

On the basis of analyzing the pros and cons of different urbanization level measurement methods, this paper puts forward urbanization level measurement method based on entity space and then uses entity space data to describe the urbanization rate of various districts and counties of JingJinJi Region in 2005 and 2009. According to the analysis of time and space differences on such basis, JingJinJi may be divided from high to low order according to the urbanization rate of the entity space: central urban area, urban development area and suburb, which respectively bear different functions. According to the comparison of entity space urbanization rate and population urbanization rate in the aspects of sequence, primacy ratio, zipf index, etc., we find the non-intensive land use status quo of urban development area of JingJinJi and the feature that the distribution of entity space urbanization scale is much uniform than distribution of population urbanization scale. Accordingly, the paper puts forward the following suggestions : (1) The space construction should depend upon function division; (2) The urban development area should pay attention to the construction of the supporting facilities and meanwhile enhance land use rate; (3) The urban construction should be enhanced in small and middle regions and counties surrounding Beijing and Tianjin.

In this study, we present a novel measurement approach for systemic risk by considering an indirect network structure. In a departure from previous studies, this measurement method captures spillovers arising from deleveraging and price impact in financial systems and calculates the amplification of losses during the contagion process. We show the relationship between a bank's vulnerability and its network connections. Applying the model to Chinese banks, we evaluate the fire-sale loss of each bank and quantify the impact of each asset in different simulated stress scenarios. Using both theoretical and empirical evidence, we show the ability of network centrality to explain systemic risk contribution: a bank with more network connections is systemically more important. We also present an optimal strategy to mitigate and govern systemic risk. Our result implies that the systemic importance of a bank is based not only on its size but also on the kinds of assets it holds; it provides useful systemic risk monitoring tools complementary to those currently used by supervisors. [ABSTRACT FROM AUTHOR]

This article examines the role of oil price uncertainty measured by the crude oil volatility index (OVX) in the risk‐return relation of stock markets from oil‐importing and exporting countries with the extended GARCH‐M models. It is found that oil price uncertainties have significant impacts on the stock risk‐return relationship in oil importers and exporters. Specifically, there is a positive risk‐return relation during the decreasing period of oil price uncertainty. This positive correlation will be undermined and become negative during the rising period of oil price uncertainty in most countries studied. What's more, change in oil price uncertainty negatively affects the stock risk‐return relation in general, and it has a more significant asymmetric effect in oil exporters than that in oil importers for the whole sample. In addition, we examine whether the impact of oil price uncertainty is sensitive to the global financial crisis in 2008. Our empirical results reveal that, on average, the stock risk‐return relation is more susceptible to OVX changes after the crisis period than that during the crisis period, suggesting impacts of OVX changes are undermined due to the extremely unstable global economy. [ABSTRACT FROM AUTHOR]

Using the Chinese equity market as the testing venue, this study explores how investor sentiment affects the immediate reaction of stock prices to earnings news in high‐ and low‐sentiment periods. Our key finding is that the sentiment‐driven pricing of earnings will differ between the two periods. Specifically, during high‐sentiment (low‐sentiment) periods, the stock price sensitivity to good (bad) earnings news increases (decreases) with investor sentiment, whereas the stock price sensitivity to bad (good) earnings news is unrelated to investor sentiment. Additionally, we find that the effect of sentiment is more pronounced for young, high volatility, growth and distressed stocks. However, contrary to the U.S. evidence, our results show that small stocks are not always more exposed to sentiment. Further analysis of the role of short‐sales constraints in the sensitivity of stock prices to bad earnings news implies that short sales can enhance informational efficiency. [ABSTRACT FROM AUTHOR]

This article investigates the effect of cross‐shareholdings on stock price synchronicity, as a measure of price informativeness, of the listed firms in the Chinese stock market. We gauge firms' levels of cross‐shareholdings in terms of centrality in the cross‐shareholding network. It is confirmed that it is through a noise‐reducing process that cross‐shareholdings promotes price synchronicity and reduces price delay. More importantly, this effect on price informativeness is pronounced for large firms and in the periods of market downturns. Overall, our analyses provide insights into the relation between the ownership structure and price informativeness. [ABSTRACT FROM AUTHOR]

In this article, we analyze the dynamic linkage between crude oil price and the US dollar at multi‐scale frequencies using time‐dependent intrinsic correlation analysis based on the complete ensemble empirical mode decomposition with adaptive noise. After applying a refined method to extract the trend, we reveal that the overall correlation and the long‐term trend correlation exhibit very similar patterns. The correlation coefficients between crude oil and the US dollar are negative at most of the time; however, the coefficients become positive in certain periods, such as 2013–2014 and 2017–2018. Furthermore, the negative correlations in high frequency intrinsic mode functions (IMFs), with a shorter time horizon, are weaker and display time‐varying characteristics, whereas the correlation in low frequency IMFs, with a longer time horizon, are stronger and more static. The findings of this article may have important implications for investors to construct optimal portfolio diversification. [ABSTRACT FROM AUTHOR]

In this paper, we apply two nonparametric approaches to mean absolute deviation (MAD) portfolio selection model. The first one is to use the nonparametric kernel mean estimation to replace the returns of assets with five different kernel functions. Then, we construct the nonparametric kernel mean estimation-based MAD portfolio model. The second one is to utilize the nonparametric kernel median estimation to replace the returns of assets with five different kernel functions. Then, we construct the nonparametric kernel median estimation-based MAD portfolio model. We also extend the two kinds of nonparametric approach to mean-Conditional Value-at-Risk portfolio model. Finally, we give the in-sample and out-of-sample analysis of the proposed strategies and compare the performance of the proposed models by using actual stock returns in Shanghai stock exchange of China. The experimental results show the nonparametric estimation-based portfolio models are more efficient than the original portfolio model. [ABSTRACT FROM AUTHOR]

This study investigates the relation between heterogeneous institutional investors and stock price crash risk, then explores the effect of short selling on the relationship. By using a dataset of 1064 firms from China for the 2007–2015 period, we find that different from the developed countries, in China both grey and independent institutional investors have positive effects on the stock price crash risk. Moreover, we also discover that for firms without short selling ban, positive correlation between grey institutional investors and stock price crash risk is weaker, while the relationship between independent institutional investors and the crash risk is not affected. [ABSTRACT FROM AUTHOR]