IDEAS home Printed from https://ideas.repec.org/a/taf/emetrv/v40y2021i8p750-795.html
   My bibliography  Save this article

Market integration, systemic risk and diagnostic tests in large mixed panels

Author

Listed:
  • Cindy S.H. Wang
  • Cheng Hsiao
  • Hao-Hsiang Yang

Abstract

This study investigates an AR (autoregressive)-filtered version of several conventional diagnostic tests for cross-sectional dependence in large mixed panels when both N and T are large, including the adjusted Lagrangian Multiplier test (LM), the cross-section dependence test (CD), and the Schott test. We show that conventional tests of cross-sectional dependence based on Pearson correlation coefficients could diverge if the components are not all I(0) processes and the modified tests possess the asymptotical normality property. The distinctive feature of these new tests is their ease of implementation, even though the exact time series properties of each component of a mixed panel are unknown or unobservable in practice. Simulations show that the AR-filtered version of the CD test (CDAR) performs well relative to the other testing procedures in the finite sample and computation time, especially for those cases with a large cross-sectional dimension. Given the good statistical properties of CDAR test, we also propose to use it as an early warning indicator for market risk or crisis.

Suggested Citation

  • Cindy S.H. Wang & Cheng Hsiao & Hao-Hsiang Yang, 2021. "Market integration, systemic risk and diagnostic tests in large mixed panels," Econometric Reviews, Taylor & Francis Journals, vol. 40(8), pages 750-795, September.
  • Handle: RePEc:taf:emetrv:v:40:y:2021:i:8:p:750-795
    DOI: 10.1080/07474938.2021.1889209
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07474938.2021.1889209
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/07474938.2021.1889209?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.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wang, Cindy S.H. & Fan, Rui & Xie, Yiqiang, 2023. "Market systemic risk, predictability and macroeconomics news," Finance Research Letters, Elsevier, vol. 56(C).
    2. Marek Vochozka & Svatopluk Janek & Zuzana Rowland, 2023. "Coffee as an Identifier of Inflation in Selected US Agglomerations," Forecasting, MDPI, vol. 5(1), pages 1-17, January.
    3. Wang, Cindy S.H. & Chen, Yi-Chi & Lo, Hsin-Yu, 2021. "A fresh look at the risk-return tradeoff," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).

    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:emetrv:v:40:y:2021:i:8:p:750-795. 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: the person in charge (email available below). General contact details of provider: http://www.tandfonline.com/LECR20 .

    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.