Nonlinear interdependence of the Chinese stock markets

Abdol Soofi, Zhe Li and Xiaofeng Hui

Quantitative Finance, 2012, vol. 12, issue 3, 397-410

Abstract: The methodologies and assumptions in financial integration studies are problematic and may lead to spurious empirical results. Using surrogate data analysis and the mutual prediction method of testing for nonlinear interdependence, it is feasible for an analyst, with a scant knowledge of the underlying dynamics of two dynamical systems, to show whether or not the systems are interdependent. This study applies these techniques in testing for nonlinear interdependence of three Chinese stock markets: Shanghai, Shenzhen, and Hong Kong. The empirical results of the present study indicate that the stock market series are nonlinear and that the Chinese stock exchanges are nonlinearly interdependent. Specifically, the evidence indicates that Shanghai and Shenzhen markets are bi-directionally interdependent, while Shanghai and Hong Kong as well as Shenzhen and Hong Kong markets are unidirectionally interdependent, with the direction of interdependence going from the mainland's markets to the Hong Kong market.

Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://hdl.handle.net/10.1080/14697688.2010.541488 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:12:y:2012:i:3:p:397-410

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RQUF20

DOI: 10.1080/14697688.2010.541488

Access Statistics for this article

Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral

More articles in Quantitative Finance from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().