Systemic Risk in the Chinese Stock Market Under Different Regimes: A Sector-Level Perspective

With the increasing importance of the stock market, it is of great practical significance to accurately describe the systemic risk of the stock market and conduct more accurate early warning research on it. However, the existing research on the systemic risk of the stock market lacks multi-dimensional factors, and there is still room for improvement in the forecasting model. Therefore, to further measure the systemic risk profile of the Chinese stock market, establish a risk early warning system suitable for the Chinese stock market, and improve the risk management awareness of investors and regulators. This paper proposes a combination model of EEMD-LSTM, which can describe the complex nonlinear interaction. Firstly, 35 stock market systemic risk indicators are selected from the perspectives of macroeconomic operation, market cross-contagion and the stock market itself to build a comprehensive indicator system that conforms to the reality of China. Furthermore, based on TEI@I complex system methodology, an EEMD-LSTM model is proposed. The EEMD method is adopted to decompose the composite index sequence into intrinsic mode function components (IMF) of different scales and one trend term. Then the LSTM algorithm is used to predicted and model the decomposed sub-sequences. Finally, the forecast result of the composite index is obtained through integration. The empirical results show that the stock market systemic risk index constructed in this paper can effectively identify important risk events within the sample period. In addition, compared with the benchmark model, the EEMD-LSTM model constructed in this paper shows a stronger early warning ability for systemic financial risks in the stock market.

Chinese stock investors, whether he is an individual investor or working for a investment company, are always worried about the risk of their asset, it is difficult to measure the risk without a functional model, thus, using a model to solve it is of the pivot. The initial aim of this paper is to measure and predict the risk of Chinese stock market using GARCH-VAR model. By analyzing historical stock index return data, this paper develops a dynamic model to capture the volatility and risk correlation of the stock market. The comprehensiveness and accuracy of the results are ensured by using recent data, including stock indices that broadly cover different industries and market capitalization. The findings suggest that significant volatility and risk correlations exist in the Chinese stock market. This paper provides an effective methodology to measure and predict the level of risk in the stock market, which provides investors and risk management organizations with valuable references and decision-making bases and is important for understanding and managing the risk in the Chinese stock market, which helps stock investors to make informed optimal decisions on risk management and asset allocation.

The year 2008 witnessed the greatest joint stock reform and financial crisis in Chinese history. After these two cases, significant changes have taken place in investors’ behaviors worldwide, along with which is the occurrence of structure change in stock market. In this paper, we employ copula model to simulate the joint distribution between Shanghai Stock Index (SSE) and Chinese Shanghai Index 300 (CSI 300), to find out structure change in Chinese stock market before and after 2008. From results of empirical studies, we get conclusions that the main nature of Chinese stocks market is symmetric, in both marginal and joint distributions. Via the changes of Copula types, upper and lower tail coefficients and Kendall coefficients, we can measure the structure change in Chinese stock market, and get further conclusion about investors’ behaviors change. Before 2008, there is an equal power in quitting market and longing, while diversified investors adjusted their expectation uniformly after this year. Testing results show that the general dependence structure of CSI 300 and SSE is highly dependent and symmetric in most cases. From the distribution of upper and lower tail coefficients, we can draw the conclusion that stratified investors are mainly focused on two tasks, after this year, to close the position on stocks with high correlated stocks market and to maintain market value of stocks.

Whether the long-memory property is inherent in the movement of the stock market returns and volatility (risk) time series is known as a very important issue practically or theoretically regard to the efficiency of the stock market. The efficient market hypothesis describes that the information obtained from past statistics can not be used to predict the future stock price. This is because when generating the information that may affect the price of the stock reflects this value of the information on the price quickly and enough. Since the future information is unknown, the future stock price will not be predictable if the stock market is efficient. However, if the long-memory property exists in the stock market returns and volatility time series, it could predict a certain portion of the future returns and risks by using past data. This predictability means the assumption of classical investment theory, that the stock market is efficient, may not be proper. Thus, the existence of long-memory property has been addressed as an important research topic by the financial investment researchers and stock market investors. By using the stock prices of 50 stocks representing the Chinese stock market and their weighted average statistical index - SSE 50 Index, this study analyzes whether the long-memory property is inherent in Chinese stock market price movement as well as explains whether the existence of long-memory property is spurious result of the contemporaneous aggregation. The Chinese stock market is extremely proper market to perform the research related to the long-memory property because it is large and highly dynamic market. Using the returns and volatility of daily closing price (i.e. the absolute value of returns and its squared value) from January 2, 2004 to December 10, 2014 to conduct the Lo’s modified (R/S) analysis and the Geweke-Porter-Hudak (GPH) test. The main results of the empirical analysis from this study are as follows. First, although SSE 50 Index return series has long-memory property, there are not many evidences for its 50 constituent company stock prices. This means that predicting the return series for SSE 50 Index is relatively easier than individual stock prices. Second, in the case of volatility, both of the SSE 50 Index and its 50 individual stock prices have the presence of a long-memory property. Third, most of the 50 individual stock prices in Chinese market have the long-memory property. These are the unique properties inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. Fourth, volatility has the stronger presence of a long-memory property than returns. This means that predicting the risk is relatively easier than returns due to volatility clustering. Based on the overall statistical test results, volatility has the stronger presence of a long-memory property than returns. The long-memory property exists in the Chinese stock market and this is the unique property inherent in the stock market time series instead of causing by the spurious consequence of a contemporaneous aggregation bias. These analytical findings indicate that the Chinese stock market is not fully efficient due to the existence of the long-memory property. The reasons that Chinese stock market is not efficient enough are that many Chinese investors have the speculative purposes and market information is not delivered transparently and quickly. Because of these characteristics, global investors will have room to reduce the risk and increase profits by leveraging long-memory properties in the Chinese stock market.

Economic globalization promotes closer economic ties among countries, and may also lead to a wider spread of economic risks. It is of great significance to study the transmission mechanism of financial risk. In this paper, the multi-scale tail risk transmission mechanism of Chinese and Russian stock market based on spatiotemporal Kriging model is studied. In this paper, the stock market of China and Russia is taken as the research object. Based on the Kriging model of time and space, the variance decomposition is studied. In this paper, we find that the maximum value of Ru share is 2.035% in the results of variance decomposition of SH series and 0.861% in the results of variance decomposition of Ru series. The multi-scale tail risk of Russian stock market has a relatively significant impact on Chinese market, while the multi-scale tail risk of Chinese stock market has a relatively low impact on Russian market.

In the context of COVID-19, this paper compares the stock market performance and financial policies between China and the United States, as well as the similarities and differences of SARS in 2002, the U.S. subprime mortgage crisis in 2008, the U.S.-China trade frictions in 2018 and the COVID-19 in terms of these events impact on stock markets. By reviewing literature and event studies, this paper conducts a cross-sectional comparison and a longitudinal comparison of the Chinese and U.S. stock markets respectively. The study shows that the impact of COVID-19 on China is far less than that of the U.S. China adopted a more accommodative and longer-lasting financial policy which increased the stock markets independence significantly through sudden crisis events. The findings will help scholars to understand the performance of the U.S. and Chinese stock markets under several crisis events and the changes in the Chinese stock market over time.

The proliferation of institutions has drawn an attention in the financial economists' interest on the effect of institutional trading on market volatility for a few decades. Existing research on institutional investors, however, has not considered separating a bull market from a bear market, an important macroeconomic factor, in studying the role of institutional trading in market volatility. In addition, few studies have focused on emerging markets in developing countries. Particularly, who can identify the change in macroeconomic fundamentals, make more rational decisions, and eventually dominate the Chinese emerging stock market, institutions or individuals? Addressing this gap, this study examines the impact of institutional trading on the Chinese emerging volatile markets, from bearish markets (2001-2003) to bullish markets (2004-2006), effectively integrating the institutional (de)stabilizing effect into a more completely developed perceptive. In particular, we investigate the relationship between the level of institutional ownership and a firm's abnormal returns on large market movement days. We find that institutions are able to make quicker and more rational adjustments in their trading strategies as compared to the delayed market reactions due to a change in macroeconomic trend and a supervising regulatory policy issued at the beginning of 2004. Institutions use different trading strategies from bearish markets (2001-2003) to bullish markets (2004-2006), on large market movement days, such as selling against a rising market in bearish markets while continually pushing the entire stock index up in bullish markets. Moreover, institutions successfully predict the tendency of future stock market although the Chinese stock market is more volatile than others. Meanwhile, we find that institutions buy (sell) less than individuals on large price swing days, consistent with the notion that institutions are more rational than individuals in emerging markets. Further, systematic risk in the Chinese stock market is twenty-four times than that in US, reflecting a significant difference between an emerging market and a developed market. Overall, these results indicate that institutions become a winner in the Chinese stock market.

Multi-scale risk contagion among international oil market, Chinese commodity market and Chinese stock market: A MODWT-Vine quantile regression approach

This study aims to investigate bidirectional risk spillovers between the Chinese and other Asian stock markets. To achieve this, we construct a dynamic Copula-EVT-CoVaR model based on 11 Asian stock indexes from 1 January 2007 to 31 December 2021. The findings show that, firstly, synchronicity exists between the Chinese stock market and other Asian stock markets, creating conditions for risk contagion. Secondly, the Chinese stock market exhibits a strong risk spillover to other Asian stock markets with time-varying and heterogeneous characteristics. Additionally, the risk spillover displays an asymmetry, indicating that the intensity of risk spillover from other Asian stock markets to the Chinese is weaker than that from the Chinese to other Asian stock markets. Finally, the Chinese stock market generated significant extreme risk spillovers to other Asian stock markets during the 2007–2009 global financial crisis, the European debt crisis, the 2015–2016 Chinese stock market crash, and the China–US trade war. However, during the COVID-19 pandemic, the risk spillover intensity of the Chinese stock market was weaker, and it acted as the recipient of risk from other Asian stock markets. The originality of this study is reflected in proposing a novel dynamic copula-EVT-CoVaR model and incorporating multiple crises into an analytical framework to examine bidirectional risk spillover effects. These findings can help Asian countries (regions) adopt effective supervision to deal with cross-border risk spillovers and assist Asian stock market investors in optimizing portfolio strategies.

This research examines the relationship between stock returns and volatility in Chinese stock market and finds that in contrast with the negative relation between market returns and volatility in the mature stock markets in developed countries, their relation in Chinese stock market is dependent on whether the market returns are positive or negative. The contemporary volatility or its change is the most important factor in determining market return. The relation between market returns and volatility observed in this study is consistent with a behavioral explanation with extrapolation bias and herding instinct rather than those relating to a longer-term lagged effect between return and volatility. Combination of contemporaneous and lagged perceived risk as measured by the lower partial moment (or their changes) and contemporaneous and lagged volatility (or their changes) has more explanation power for the market returns than just contemporaneous and lagged volatility (or their changes). The perceived risk and market returns have a negative relation in Chinese stock market, which is similar to the negative relation between volatility and market returns in the mature stock markets. This result shows that there is some feedback effect in the relation between market returns and perceived risk in Chinese stock market.

In Mar, 22nd 2018, the U.S. government announced the list of tariff-imposed commodities, which will impose 25% tariffs on 1,333 items worth US$50billion of goods exports from China to the U.S. The trade war between China and US has been lasted for 4 years and 5 months, the US government has increased the tariffs 6 times, and specifically, this brings a huge impact on capital flows and dynamic changes in Chinese stock market. This paper demonstrates how increase the tariff bring huge impacts on Chinese stock market by analyzing different models, such as ADF test, VAR model, and ARMA-GARCH model, and by studying the results to better understand how Chinese stock market influenced by the tariff increase, and to provide an accurate prediction for the future trend of the Chinese stock market and make recommendation for Chinese investors and Chinese government. The trade war severe impact Chinese economy, and the stock price is falling which lead to investor to hold their money instead of investing in the market, and investors rushed to the safety of low-yielding government debt. The ARMA-GARCH model analyse and present the data and represents the impacts by analysing the stock returns and the volatility. Lastly, there are some recommendations based on current economic situation.

To compare and analyze the difference between the United States and Chinese stock market, the relative variance, correlation, beta, and volatility of different stock markets are required. These parameters could help people to make better decisions and risk predictions while investing in China or the United States. The analysis also shows cases and the unique characteristics of both the Chinese and United States stock market, leading to the conclusion that the United States stock market is more mature and stable than the Chinese stock market. Through the comparison between the two stock markets, the Chinese and American economies could be better understood.

Does oil product pricing reform increase returns and uncertainty in the Chinese stock market?

Purpose – The purpose of this paper is to examine whether the framework of Prospect Theory and Mental Accounting proposed by Grinblatt and Han (2005) can be applied to analyzing the relationship between the disposition effect and momentum in the Chinese stock market. Design/methodology/approach – The paper applies the methodology proposed by Grinblatt and Han (2005). Findings – Using firm-level data, with a sample period from January 1998 to June 2013, the authors find evidence that the momentum effect in the Chinese stock market is not driven by the disposition effect, contradicting the findings of Grinblatt and Han (2005) concerning the US stock market. The discrepancies in the findings between the Chinese and US stock markets are robust and independent of sample periods. Research limitations/implications – The findings suggest that Grinblatt and Han’s model may not be applicable to the Chinese stock market. This is possibly because of the regulatory differences between the two stock markets and cross-national variation in investor behavior; in particular, the short-selling prohibition in the Chinese stock market and greater reference point adaptation to unrealized gains/losses among Chinese compared to Americans. Originality/value – This study provides evidence of the inapplicability of Grinblatt and Han’s model for the Chinese stock market, and shows the differences in the relationship between disposition effect and momentum between the Chinese and US stock markets.

This paper first proposes the generalized autoregressive score (GAS) factor X model for predicting Value‐at‐Risk (VaR) and expected shortfall (ES). This model is flexible for adding explanatory variables (X) that are informative for forecasting extreme risks and thus can improve forecast accuracy of the traditional GAS factor model. Then, to investigate whether high‐frequency data of domestic and international stock market are predictive of the extreme risk of Chinese stock market, X is set to be the corresponding realized volatility (RV) of these markets. Empirical studies based on 13 stock markets show that lagged RVs of domestic and influential international markets have prediction ability of extreme risks in Chinese stock market. The RV of the domestic market is more informative than that of international markets, among which the RVs of Asian and US markets are more predictive than European markets. We have also investigated compound information of the RVs of all markets, the results of which show that the mean of RVs is more informative than the first principle component; however, it is less informative than the individual RV of some markets. Additionally, there is no gain in forecast accuracy of the GAS factor RV models by forecast combination.