IDEAS home Printed from https://ideas.repec.org/a/taf/reroxx/v33y2020i1p698-711.html
   My bibliography  Save this article

A multivariate cointegration time series model and its applications in analysing stock markets in China

Author

Listed:
  • Yan-Yong Zhao
  • Xu-Guo Ye
  • Zhong-Cheng Han

Abstract

This paper explores nonlinear cointegration between Chinese mainland stock markets and Hong Kong stock market in a multivariate framework for the period January, 1998 to December, 2014 by a nonparametric method. The local linear kernel smoothing method is developed to estimate the unknown function, and the practical problem of implementation is also addressed. Then, a simple nonparametric version of a bootstrap test is adapted for testing misspecification. Furthermore, Some Monte Carlo experiments are presented to examine the finite sample performance of the proposed procedure. Finally, the stock markets data set is discussed in detail by using proposed procedures, showing that Shanghai Stock Index (SHSI) and Shenzhen Component Index (SZCI) can affect Hang Seng Index (HSI), and the influence appears to be a strong nonlinear characteristics.

Suggested Citation

  • Yan-Yong Zhao & Xu-Guo Ye & Zhong-Cheng Han, 2020. "A multivariate cointegration time series model and its applications in analysing stock markets in China," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 698-711, January.
  • Handle: RePEc:taf:reroxx:v:33:y:2020:i:1:p:698-711
    DOI: 10.1080/1331677X.2020.1711792
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1331677X.2020.1711792
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/1331677X.2020.1711792?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:reroxx:v:33:y:2020:i:1:p:698-711. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/rero .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.