An Empirical Research on Chinese Stock Market and International Stock Market Volatility (original) (raw)
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An Empirical Research on Chinese Stock Market Volatility Based on Garch
International Journal of Latest Research in Engineering and Technology, 2018
Stock market volatility is a major issue in the modern financial field. As China's stock market is immature and volatile, it is particularly important to study the volatility of China's stock market. This paper selects Shanghai Composite Index gains from January 7, 2013 to December 29, 2017, to make an empirical research on stock market volatility based on GARCH model. The results show that there is volatility clustering, durative and leverage effects in stock market. The volatility is largely affected by the past volatility, especially in Chinese stock market. Its influence reaches 0.927.
Modeling Volatility for the Chinese Equity Markets
2004
A series of GARCH models are investigated for the volatility of the Chinese equity data from the Shenzhen and Shanghai markets. There has been empirical evidence of volatility clustering, contrary to findings in previous studies. Each market contains different GARCH models which fit well. The models are used to test for a spill-over effect between the two Chinese markets, an example of volatility transmission within one country and between two equity exchanges. Our testing suggests that there is no volatility transmission between the two markets.
Empirical Analysis of Stock Returns and Volatility: Evidence from Asian Stock Markets
Technological and Economic Development of Economy, 2016
The objective of this research isto measure and examine volatilities among important stock markets of Asia and to ascertain a causal relation between volatility and stock returns. For this purpose six markets KSE100 (Karachi, Pakistan), BSE Sensex (Mumbai, India), NIKKEI 225 (Tokyo, Japan), Hang Seng (Hong Kong), Shanghai Stock Exchange (SSE) (Shanghai, China) and KOSPI (Seoul, South Korea) were considered. Stock market indices comprise of daily data from the period January 2002 to December 2009. The graphical representation of time series shows the preliminary examination of stock behaviors. The analysis shows the high correlation and heteroskedastic trend (volatility) among the stock markets in selected time period. After preliminary analysis the formal descriptive method of mean, standard deviation and coefficient of variation have been applied for measuring and ranking purposes. The results show that KOSPI has the highest average annual return of 12.67% and followed by BSE with 11.61%, whereas, KSE 100 has the least annual average returns of 9.31%. The highest volatility coefficient of 3.097 has been observed in Hang Seng (Hong Kong) followed by 2.87 in Nikkei (Tokyo). However, the KSE 100 observed the lowest volatility coefficient of 2.078. Bartlett's test is applied for the inferential analysis to investigate whether the equality of volatility is the same in each market return. Finally, GARCH (1, 1) model is applied which concludes a significant ARCH (1) and GARCH (1) effects and confirms all markets' returns are statistically significant since p < 0.01 and their Long Run Average Variances (LRAV) range from 1.52% to 2.54% for KSE100 Index and Shanghai Stock Exchange respectively.
Volatility in Stock Markets: A Comparison of Developed and Emerging Markets of the World
Indian Journal of Commerce & Management Studies, 2017
Volatility in markets is the growing area of crucial attention which is being analysed by many academicians over the world. The reason being that with the passage of time, the probability of deviation of the prices from the initial intrinsic value increases. In this research we have tried to model the volatility of two indices: MSCI emerging markets index and MSCI world index with the use of ARCH and GARCH models. The volatility clustering and ARCH effect were seen and the models were constructed. Both the ARCH and GARCH terms were found to be significant in both the market indices. It was found that in emerging markets, yesterday's volatility had greater influence in explaining today's volatility while in case of developed markets, both yesterday's volatility and information had immense influence in explaining today's volatility. The information is of immense use to the finance professionals and investors and can help them in taking correct portfolio decisions.
An Application of GARCH while investigating volatility in stock returns of the World.
A healthy stock market is a sign of sound and healthy economy. Stock market is a volatile market affected, at times directly and most often indirectly, by many micro and macroeconomic players. Of these players interest rates and exchange rates are among the ones undertaken in this study. The rationale behind this study is to ascertain the volatility in stock returns of various stock exchanges in relevance to interest rates and exchange rates over a range of 8 countries for assorted periods. GARCH (1, 1) was deployed for investigating the possible eventualities of volatilities of stock markets. The findings were found varying for Pakistan, India, Hong Kong, Japan, United States, United Kingdom, Spain and Germany. Moreover, almost for all countries GARCH (1, 1) yielded significant results confirming the existence of volatility of stock markets for the current period of outlined countries due to volatility of those stock markets during the previous lags. The findings may help investors know the stock markets’ trends which are also for some cases (nations) affected by interest rates and/or exchange rates and thus to invest accordingly.
Empirical Research on Shanghai Stock Index Based on GARCH Model
International Journal of Latest Research in Engineering and Technology, 2018
The stock market has been affected by many factors, leading to the stock market is unpredictable, which makes stocks have high-risk and high-yield characteristics. Studying the stock market's Shanghai stock Index is the key to reducing risks and increasing profits. This article analyzes the characteristics of the daily yield series by collecting the daily closing price of the Shanghai stock Index from the daily closing price of June 3, 2013 to June 29, 2018, and using Eviews statistical analysis software to analyse the nature of the sequence, the time series model GARCH(1,1) is initially fitted. The empirical results show that the GARCH(1,1) model has a good fitting effect on the time series of the logarithmic price of the Shanghai Stock Index.
Study on the Dynamic Correlation between Chinese and American Financial Markets
2020
With the deepening of the economic globalization and finance liberalization, the financial links between open economies are becoming closer and closer, and the interconnectivity between international financial markets is gradually increasing. With the support of DCC-GARCH model, the paper conducts an empirical study towards the dynamic correlation between American and Chinese stock markets. The result indicates that the logarithmic return series of Shanghai Composite index and S&P 500 index both show apparent volatility clustering and constancy. The correlation between them shows significant time variability, but the dynamic degree of their correlation is relatively low.
2001
This paper investigates the time-series behavior of stock returns for seven Asian stock markets. In most cases, higher average returns appear to be associated with a higher level of volatility. Testing the relationship between stock returns and unexpected volatility, the evidence shows that four out of seven Asian stock markets have significant results. Further analyzing the relationship between stock returns and time-varying volatility by using Threshold Autoregressive GARCH(1,1)-in-mean specification indicates that the null hypothesis of no asymmetric effect on the conditional volatility is rejected for the daily data. However, the null cannot be rejected for the monthly data.
Stock Market Volatility Analysis: A Case Study of TUNindex
2020
Volatility is directly associated with risks and returns. This study aims to examine the volatility characteristics on Tunisian stock market index (5 days a weak TUNindex) that include clustering volatility, leptokurtosis, and leverage effect. The first objective is then to use the GARCH type models to estimate volatility of the daily returns series, consisting of 2191 observations from 01/02/2011 to 19/11/2019, with no significant weekdays effect. We use both symmetric and asymmetric models. The main findings suggest that the symmetric GARCHM and asymmetric TGARCH /APGARCH models can capture characteristics of TUNindex whereas EGARCH reveals no significant support for leverage effect existence. Looking at news impact curves, GJR model appears to be relatively better than other models. However, the volatility of stock returns is more affected by the past volatility than the related news from the previous period. The second objective is to use GARCHM- X S models to capture the effect...
ANALYSIS OF VOLATILITY DYNAMICS IN SELECTED SECTORAL INDICES OF NATIONAL STOCK EXCHANGE
This study is based on five consumer specific sectoral indices as Auto, Bank, FMCG, IT and Realty of National Stock Exchange, India especially after recession period. The main purpose of this study is to examine the dynamics of volatility in these five Sectoral indices. The volatility dynamics such as volatility clustering, volatility persistence and leverage effect in these sectors are investigated by using three GARCH Family models to know the status of these sectors after recession period. After implementing GARCH family models it is found that Nifty and all five sectors except IT are highly volatile and volatility moves in clusters. Significant ARCH and GARCH terms of these models indicate that current period variance of stock returns is conditional on previous period volatility in all five sectors except IT. Significant Leverage effect is captured in all sectors except FMCG sector in EGARCH model indicating negative shocks have larger impact on volatility than positive shocks. In EGARCH (1, 1) and TGARCH (1, 1) Auto and realty both have shown less volatility persistence means there is faster decay of volatility shocks in these two sectors. So the risk averse investors can invest in IT, auto and Realty sectors by avoiding bank and FMCG sectors stocks where volatility persist for a longer duration. In overall all five sectors are suitable to invest. Keywords - Sectoral indices, GARCH, EGARCH and TGARCH.