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The purpose of this paper is to explore the dynamic interdependence structure and risk spillover effect between the Chinese stock market and the US stock market.

This paper mainly uses the multivariate R-vine copula-complex network analysis and the multivariate R-vine copula-CoVaR model and selects stock price indices and their subsector indices as samples.

The empirical results indicate that the Energy, Materials and Financials sectors have leading roles in the interdependent structure of the Chinese and US stock markets, while the Utilities and Real Estate sectors have the least important positions. The comprehensive influence of the Chinese stock market is similar to that of the US stock market but with smaller differences in the influence of different sectors of the US stock market on the overall interdependent structure system. Over time, the interdependent structure of both stock markets changed; the sector status gradually equalized; the contribution of the same sector in different countries to the interdependent structure converged; and the degree of interaction between the two stock markets was positively correlated with the degree of market volatility.

This paper employs the methods of nonlinear cointegration and the R-vine copula function to explore the interactive relationship and risk spillover effect between the Chinese stock market and the US stock market. This paper proposes the R-vine copula-complex network analysis method to creatively construct the interdependent network structure of the two stock markets. This paper combines the generalized CoVaR method with the R-vine copula function, introduces the stock market decline and rise risk and further discusses the risk spillover effect between the two stock markets.

The non-observed economy (NOE) is a pervasive phenomenon worldwide, especially in developing countries, but the size of the NOE and its contributions to the overall economy are usually unknown. This paper presents an estimation of the average size of the NOE for the 31 provincial regions in China between 1992 and 2013.

This study uses the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data combined with 11 existing surveys on or measurements of NOE for 191 countries or regions throughout the world, to measure the size of the NOE.

The results show that the NOE share is unevenly distributed among China's provincial regions, with the smallest being 3.19% for Beijing and the largest being 69.71% for Ningxia. The national average is 43.11%, while the figures for the eastern region, middle region, northeastern region and western region are 39.3%, 47.6%, 44.7% and 43.6%, respectively. The NOE estimates are negatively correlated with the measured gross domestic product (GDP) and GDP per capita, which suggests that developed regions tend to have less NOE.

The nighttime lights are used to measure the NOE for China's provincial regions. Compared with traditional databases, one of the prominent features of nighttime lights is its objectivity, as there is little human interference; therefore, it can be used to achieve more accurate results.

The purpose of this paper is to pay more attention to four different research questions at least. One is that this study intends to explore the changes of the risk-return relationship over time, because the institutions and environment have changed a lot and might tend to influence the risk-return regime in the Chinese stock markets. The second question is whether there is any difference for the risk-return relationship between Shanghai and Shenzhen stock markets. The third question is to compare the similarities and dissimilarities of the risk-return tradeoff for different frequency data. The fourth question is to compare the explanation power of different GARCH-M type models which are all widely used in exploring the risk-return tradeoff.

This paper investigates the risk-return tradeoff in the Chinese emerging stock markets with a sample including daily, weekly and monthly market return series. A group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. Additionally, some diagnostic checks proposed by Engle and Ng (1993) are used in this paper, and this will help to assess the robustness of different models.

The empirical results show that the dynamic risk-return relationship is quite different between Shanghai and Shenzhen stock markets. A positive and statistically significant risk-return relationship is found for the daily returns in Shenzhen Stock Exchange, while the conditional mean of the stock returns is negatively related to the conditional variance in Shanghai Stock Exchange. The risk-return relationship usually becomes much weaker for the lower frequency returns in both markets. A further study with the sub-samples finds a positive and significant risk-return trade-off for both markets in the second stage after July 1, 1999.

This paper extends the existing related researches about the Chinese stock markets in several ways. First, this study uses a longer sample to investigate the relationship between stock returns and volatility. Second, this study estimates the returns and volatility relationship with different frequency sample data together. Third, a group of variant specifications of GARCH-M type models are used to test the risk-return tradeoff. In particular, the author employs the Component GARCH-M model which is relatively new in this line of research. Fourth, this study investigates if there is any structural break affecting the risk-return relationship in the Chinese stock markets over time.