Stock Exchanges In China Research Articles

Complex dynamical behaviors of daily data series in stock exchange

It is well known that many economic data series show chaotic behaviors. In this Letter, we further investigate the complex dynamical behaviors of the daily data series, including opening quotation, closing quotation, maximum price, minimum price, and total exchange quantum, in Shenzhen stock exchange and Shanghai stock exchange, which are two representative stock exchanges in mainland China. The maximum Lyapunov exponents, correlation dimensions, and frequency spectra are calculated for these time series. Our results indicate that some daily data series of stock exchanges display low-dimensional chaotic behaviors, and some other daily data series do not show any chaotic behavior. Moreover, we introduce a weighted one-rank local-region approach for predicting short-term daily data series of stock exchange.

STOCK RETURNS AND VOLATILITY ON CHINA'S STOCK MARKETS

AbstractWe examine time‐series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance ratio tests reject the hypothesis that stock returns follow a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time‐varying volatility and shows volatility is highly persistent and predictable. The results of GARCH‐M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns.JEL classification: G15

Stock Returns and Volatility: an empirical study of Chinese stock markets

This paper uses GARCH models to analyse the relationship between returns and volatility on the Shanghai and Shenzhen Stock Exchanges in China. Empirical estimates using the sample data from 21 May 1992 to 2 February 1996 suggest that the variances of the returns in the two markets are best modeled by the GARCH-M (1,1) specification. Volatility transmission between the two markets (the volatility spill-over effect) is also found to exist. The results of one month ahead ex ante forecasts show that the conditional variances of the returns of the two stock markets exhibit a similar pattern.

Internal Regulation: The Effects of Government Ownership on the Value of the Firm

Previous articleNext article No AccessInternal Regulation: The Effects of Government Ownership on the Value of the FirmCatherine C. Eckel and Theo VermaelenCatherine C. Eckel Search for more articles by this author and Theo Vermaelen Search for more articles by this author PDFPDF PLUS Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by The Journal of Law and Economics Volume 29, Number 2Oct., 1986 Sponsored by The University of Chicago Booth School of Business and The University of Chicago Law School Article DOIhttps://doi.org/10.1086/467124 Views: 14Total views on this site Citations: 28Citations are reported from Crossref Copyright 1986 The University of ChicagoPDF download Crossref reports the following articles citing this article:Qing Hu, Wenjing Li, Chen Lin, Lai Wei Trade-induced competition and ownership dynamics, Journal of Development Economics 160 (Jan 2023): 102979.https://doi.org/10.1016/j.jdeveco.2022.102979Aidan Vining, Mark Moore, Claude Laurin Listed public–private enterprises: stock market information, agency costs and productive efficiency outcomes, International Journal of Public Sector Management 35, no.44 (Aug 2021): 388–409.https://doi.org/10.1108/IJPSM-02-2021-0050Veljko Fotak, Haekwon Lee Public-private co-lending: Evidence from syndicated corporate loans, Journal of Banking & Finance 119 (Oct 2020): 105898.https://doi.org/10.1016/j.jbankfin.2020.105898Matteo Landoni Knowledge creation in state-owned enterprises, Structural Change and Economic Dynamics 53 (Jun 2020): 77–85.https://doi.org/10.1016/j.strueco.2020.01.001Abhinav Goyal, Shrikant P. 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