The Chinese Stock Market Does not React to the Japanese Market: Using Intraday Data to Analyse Return and Volatility Spillover Effects

2016 ◽  
Vol 67 (3) ◽  
pp. 280-294 ◽  
Author(s):  
Yusaku Nishimura ◽  
Yoshiro Tsutsui ◽  
Kenjiro Hirayama
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
Japanese Market ◽  
Intraday Data

scholarly journals Volatility Modelling of Chinese Stock Market Monthly Return and Investor Sentiment Using Multivariate GARCH Models

2020 ◽  
Vol 5 (1) ◽  
pp. 123-133
Author(s):  
Hongjun Zeng
Keyword(s):  
Stock Market ◽  
Investor Sentiment ◽  
Multivariate Garch ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
Volatility Spillovers ◽  
Stock Market Return ◽  
Monthly Return ◽  
Multivariate Garch Models ◽  
Linkage Effects

This article examines the linkage and volatility spillover among Chinese Stock Market Monthly Return and Investor Sentiment, investigating the effect dynamic links of various investor sentiment indicators and Chinese stock market return volatility. Employing the DCC and BEKK GARCH, we find investor sentiment is to some extent linked to the yield fluctuations of the Chinese stock market, but the volatility spillover is relatively weak. In the test period (2005-2020), we observe that several indicators do not explain their linkage effects with CSI 300 index of return fluctuations and volatility spillovers well, with no indicators can reflect both of these effects. Most indicators are linkage with the CSI 300 index, especially consumer confidence index (CCI), new investor account openings last month (NIA) and the volume of transactions last month (TURN) have significant linkage effects with the CSI 300 index. We also find that only the CCI index has a one-way volatility spillover on the CSI 300 index, and the CSI 300 index has no volatility spillover on any indicator.

A Study on Information Spillover Effects from NASDAQ 100 to KOSDAQ 50 Index Futures Markets

2003 ◽  
Vol 11 (1) ◽  
pp. 145-167
Author(s):  
Gyu Hyeon Mun ◽  
Jeong Hyo Hong
Keyword(s):  
Stock Market ◽  
Real Time ◽  
Futures Markets ◽  
Spillover Effects ◽  
Time Varying ◽  
Volatility Spillover ◽  
Index Futures ◽  
Conditional Mean ◽  
Time Basis ◽  
Information Spillover

This paper studies the information spillover effects over price and volatility across countries by using open-to-close (daytime) returns and close-to-open (overnight) returns of NASDAQ 100 and KOSDAQ 50 index futures data from January 1, 2001 to December 31, 2001. Based on the time-varying AR(1)-GARCH (1,1)-M models, we document that statistically significant conditional mean and volatility spillover effects from the daytime returns of NASDAQ 100 index futures to both overnight returns and daytime returns of KOSDAQ 50 index futures were observed. We also find that there were information spillover effects from overnight returns of NASDAQ 100 index futures to daytime returns of KOSDAQ 50 index futures returns because investors in Korean stock markets can get information on U.S. stock market movement on real time basis due to the ECN transaction with its trading hour overlapped. Finally, we find that the daytime returns of KOSDAQ 50 index futures significantly influence the overnight and daytime returns of the NASDAQ 100 index futures.

Transmission of shocks between Chinese financial market and oil market

2019 ◽  
Vol 15 (2) ◽  
pp. 262-286 ◽  
Author(s):  
Mouna Abdelhedi ◽  
Mouna Boujelbène-Abbes
Keyword(s):  
Stock Market ◽  
Wavelet Decomposition ◽  
Price Index ◽  
Garch Model ◽  
Continuous Wavelet ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
Decomposition Technique ◽  
Content Type ◽  
Oil Market

Purpose The purpose of this paper is to empirically investigate the volatility spillover between the Chinese stock market, investor’s sentiment and oil market, specifically during the 2014‒2016 turmoil period. Design/methodology/approach This study used the daily and monthly China market price index, oil-price index and composite index of Chinese investor’s sentiment. The authors first use the DCC GARCH model in order to study the correlation between variables. Second, the authors use a continuous wavelet decomposition technique so as to capture both time- and frequency-varying features of co-movement variables. Finally, the authors examine the spillover effects by estimating the BEKK GARCH model. Findings The wavelet coherency results indicate a substantial co-movement between oil and Chinese stock markets in the periods of high volatility. BEKK GARCH model outcomes confirm this relation and report the noteworthy bidirectional transmission of volatility between oil market shocks and the Chinese investor’s sentiment, chiefly in the crisis period. These results support the behavioral theory of contagion and highlight that the Chinese investor’s sentiment is a channel through which shocks are transmitted between the oil and Chinese equity markets. Thus, these results are important for Chinese authorities that should monitor the investor’s sentiment to better control the interaction between financial and real markets. Originality/value This study makes three major contributions to the existing literature. First, it pays attention to the recent 2015 Chinese stock market bumble. Second, it has gone some way toward enhancing our understanding of the volatility spillover between the investor’s sentiment, investor’s sentiment variation, oil prices and stock market returns (variables of interest) during oil and stock market crises. Third, it uses the continuous wavelet decomposition technique since it reveals the linkage between variables of interest at different time horizons.

scholarly journals Half-day trading and spillovers

2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Yifan Chen ◽  
Limin Yu ◽  
Jianhua Gang
Keyword(s):  
Stock Market ◽  
Spillover Effects ◽  
The United States ◽  
Time Varying ◽  
Chinese Stock Market ◽  
Conditional Correlation ◽  
Day Trading ◽  
Market Impacts ◽  
Positive Spillover ◽  
The U.S

AbstractThis paper investigates the linkage of returns and volatilities between the United States and Chinese stock markets from January 2010 to March 2020. We use the dynamic conditional correlation (DCC) and asymmetric Baba–Engle–Kraft–Kroner (BEKK) GARCH models to calculate the time-varying correlations of these two markets and examine the return and volatility spillover effects between these two markets. The empirical results show that there are only unidirectional return spillovers from the U.S. stock market to the Chinese stock market. The U.S. stock market has a consistently positive spillover to China’s next day’s morning trading, but its impact on China’s next day’s afternoon trading appears to be insignificant. This finding implies that information in the U.S. stock market impacts the performance of the Chinese stock market differently in distinct semi-day trading. Moreover, with respect to the volatility, there are significant bidirectional spillover effects between these two markets.

Spillover effects between energies, gold, and stock: the United States versus China

2020 ◽  
Vol 31 (8) ◽  
pp. 1416-1447 ◽  
Author(s):  
Xie He ◽  
Tetsuya Takiguchi ◽  
Tadahiro Nakajima ◽  
Shigeyuki Hamori
Keyword(s):  
United States ◽  
Stock Market ◽  
Crude Oil ◽  
Spillover Effect ◽  
The United States ◽  
Commodity Markets ◽  
Volatility Spillover ◽  
Chinese Stock Market ◽  
The Us ◽  
Volatility Spillover Effect

This study investigates the time–frequency dynamics of return and volatility spillovers between the stock market and three commodity markets: natural gas, crude oil, and gold via a comparative analysis between the United States and China is conducted with the help of new empirical methods. Our findings are as follows. First, in terms of time, return spillovers between crude oil and the stock market are strongest in two of the three commodity markets. Crude oil emits a net negative return spillover to the US stock market, and a net positive return spillover to the Chinese stock market. By contrast, the strongest volatility spillover effect is transmitted to the stock markets of both countries through gold. However, gold has a net positive volatility spillover effect on the US stock market and a net negative effect on the Chinese stock market. In the frequency domain, most of the return spillover is produced in the short term, and most of the volatility spillover occurs in the long term. In addition, the moving-window method reveals the dynamic nature of the spillover effect. Some extreme events can have a dramatic effect on the spillover index. Conversely, the spillover effect differs significantly between the two countries and is characterized by time variation and frequency dependence.

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