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The objective of this study is to analyze systemic importance of Chinese financial institutions and its influential factors based on return spillover network. We first investigate the return spillover effects among financial institutions and construct the return spillover networks by Granger causality in vector autoregressive (VAR) models. Then we calculate six network centralities (degree centrality, closeness centrality, betweenness centrality, modified Katz centrality, eccentricity centrality and information centrality) to measure systemic importance of financial institutions. Because different centrality measures are correlated with each other, we use the principal component analysis method to obtain comprehensive information about systemic importance of financial institutions. Finally, we identify the major factors, including market and accounting variables, which affect systemic importance of the financial institutions with panel data regression analysis. We find that financial institutions with larger tail risk of stock return, higher return on equity, lower turnover rate and lower assets growth rate tend to be associated with greater systemic importance. [ABSTRACT FROM AUTHOR]

Abstract This study conducts a theoretical analysis about effects of China's financial markets on the global commodity prices, and employs ARDL model and SVAR model to test it empirically. Results illustrated that: CNY NDF rate has exerted impact on some industrial metals before the Global Financial Crisis of 2008, while impact of China's stock market on commodity prices is insignificant. We find the influence of China's stock market and CNY NDF market significantly increased after the crisis, which means the fluctuations of China's financial markets do influence the commodity prices. However, such effect is still weak comparing with US stock market and FX market. Highlights • China's financial markets are supposed to influence commodity price through "macro-economy reflection", financial market information transmission and market sentiment contagion channel. • The impact of China's stock market on some industrial metals is significant while the stock market is insignificant before the GFC. • The influence of China's financial market significantly increased after the GFC • Chinese financial markets' impact is still weak. [ABSTRACT FROM AUTHOR]

Abstract Using stock market returns of two stock exchanges in China, this paper employs MF-DCCA to investigate the non-linear cross-correlation between individual investor sentimentand Chinese stock market return. We find that there exists a power-law cross-correlation between individual investor sentiment and Chinese stock market return, and the cross-correlations are significantly multifractal. In addition, the cross-correlation between individual investor sentiment and Shenzhen Component Index (SZSE) return is more anti-persistent than that between individual investor sentiment and Shanghai Composite Index (SSEC) return, implying that individual investor sentiment has a stronger impact on small stocks. Besides, long-range correlations, fat-tailed distribution and extreme value all contribute to the multifractality for cross-correlation between individual investor sentiment and SSEC return, while long-range correlation is the main source of multifractality for the cross-correlation between individual investor sentiment and SZSE return. Highlights • Cross-correlation between investor sentiment and stock return is investigated. • Yu'ebao Sentiment Index and Shenzhen stock market return is more anti-persistent. • The sources of multifractal features are investigated. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of hot money on the return and volatility of the Chinese stock market using a nonlinear Granger causality test and a new GARCH-class model based on mixed data sampling regression (GARCH–MIDAS). The empirical results suggest that no linear or nonlinear causality exists between the growth rate of hot money and the Chinese stock market return, implying that the Chinese stock market is not driven by hot money and vice versa. However, hot money has a significant positive impact on the long-term volatility of the Chinese stock market. Furthermore, the dependence between the long-term volatility caused by hot money and the total volatility of the Chinese stock market is time-variant, indicating that huge volatilities in the stock market are not always triggered by international speculation capital flow and that Chinese authorities should further focus on more systemic reforms in the trading rules and on effectively regulating the stock market. [ABSTRACT FROM AUTHOR]

In this paper, we advocate the provincial TV audience rating as the novel proxy for the provincial investor sentiment (PIS) and investigate its relation with stock returns. The empirical results firstly show that the PIS is positively related to stock returns. Secondly, we provide direct evidence on the existence of home bias in China by observing that the provincial correlation coefficient is significantly larger than the cross-provincial correlation coefficient. Finally, the PIS can explain a large proportion of provincial comovement. To sum up, all these findings support the role of the non-traditional information sources in understanding the “anomalies” in stock market. [ABSTRACT FROM AUTHOR]

Abstract: As an aggregate measure of the variations in individuals, the analysis of mobility provides a substantial and comprehensive perspective into the complexity of socio-economic systems. In this paper, we introduced the ranking mobility index to measure the ranking variations of the stocks in Chinese stock market over time. Using the daily data of 837 constituent stocks of the Shanghai A-Stock Composite Index from January 1, 2002 to December 31, 2012, we examined respectively the dependence of ranking mobility with respect to the absolute return, trading volume and turnover ratio on the sampling time interval. The scaling property is observed in all three relations. The fact of long relaxation times gives evidence of long memory property in the stock ranking orders. [Copyright &y& Elsevier]

Abstract: We investigate the cross-correlation between price returns and trading volumes for the China Securities Index 300 (CSI300) index futures, which are the only stock index futures traded on the China Financial Futures Exchange (CFFEX). The basic statistics suggest that distributions of these two time series are not normal but exhibit fat tails. Based on the detrended cross-correlation analysis (DCCA), we obtain that returns and trading volumes are long-range cross-correlated. The existence of multifractality in the cross-correlation between returns and trading volumes has been proven with the multifractal detrended cross-correlation analysis (MFDCCA) algorithm. The multifractal analysis also confirms that returns and trading volumes have different degrees of multifractality. We further perform a cross-correlation statistic to verify whether the cross-correlation significantly exists between returns and trading volumes for CSI300 index futures. In addition, results of the test for lead-lag effect demonstrate that contemporaneous cross-correlation of return and trading volume series is stronger than cross-correlations of leaded or lagged series. [Copyright &y& Elsevier]

Abstract: We perform return interval analysis of 1-min realized volatility defined by the sum of absolute high-frequency intraday returns for the Shanghai Stock Exchange Composite Index (SSEC) and 22 constituent stocks of SSEC. The scaling behavior and memory effect of the return intervals between successive realized volatilities above a certain threshold are carefully investigated. In comparison with the volatility defined by the closest tick prices to the minute marks, the return interval distribution for the realized volatility shows a better scaling behavior since 20 stocks (out of 22 stocks) and the SSEC pass the Kolmogorov–Smirnov (KS) test and exhibit scaling behaviors, among which the scaling function for 8 stocks could be approximated well by a stretched exponential distribution revealed by the KS goodness-of-fit test under the significance level of 5%. The improved scaling behavior is further confirmed by the relation between the fitted exponent and the threshold . In addition, the similarity of the return interval distributions for different stocks is also observed for the realized volatility. The investigation of the conditional probability distribution and the detrended fluctuation analysis (DFA) show that both short-term and long-term memory exists in the return intervals of realized volatility. [Copyright &y& Elsevier]

As the largest coal and the second largest crude oil consumer in the world, China has urgent task to develop its renewable energy industry quickly. By now, there are over 80 renewable energy companies listed in China's stock exchanges. Thus to manage the market risk and achieve better investment returns of renewable energy stocks in China is of great value for policy makers and investors. The aim of this paper is to introduce an innovative portfolio allocation approach, robust portfolio, to improve portfolio performance of renewable energy stocks in China by considering the parameter uncertainty in the process of portfolio optimization. Furthermore, to make the conclusions more robust, we classify the stock market into three commonly recognized statuses: bull market, bear market and steady market, respectively, and compare the performances of robust portfolio method with traditional Markowitz approach. The empirical results indicate that the robust portfolio method can produce better performance of the renewable energy stock portfolio than Markowitz approach in various market statuses with much more flexibility in handling the problem of parameter uncertainty. This paper provides an alternative but very effective strategy other than Markowitz method for the portfolio allocation of renewable energy stocks in China. • Renewable energy stocks in China are analyzed in a portfolio allocation framework. • Robust portfolio method is utilized to improve the portfolio performance. • Both in-sample and out-of-sample performances of the renewable energy stocks are evaluated. [ABSTRACT FROM AUTHOR]