IDEAS home Printed from https://ideas.repec.org/a/taf/jnlbes/v34y2016i2p240-253.html
   My bibliography  Save this article

A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion

Author

Listed:
  • Bertrand Candelon
  • Sessi Tokpavi

Abstract

This article introduces a kernel-based nonparametric inferential procedure to test for Granger causality in distribution. This test is a multivariate extension of the kernel-based Granger causality test in tail event. The main advantage of this test is its ability to examine a large number of lags, with higher-order lags discounted. In addition, our test is highly flexible because it can be used to identify Granger causality in specific regions on the distribution supports, such as the center or tails. We prove that the test converges asymptotically to a standard Gaussian distribution under the null hypothesis and thus is free of parameter estimation uncertainty. Monte Carlo simulations illustrate the excellent small sample size and power properties of the test. This new test is applied to a set of European stock markets to analyze spillovers during the recent European crisis and to distinguish contagion from interdependence effects.

Suggested Citation

  • Bertrand Candelon & Sessi Tokpavi, 2016. "A Nonparametric Test for Granger Causality in Distribution With Application to Financial Contagion," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 240-253, April.
  • Handle: RePEc:taf:jnlbes:v:34:y:2016:i:2:p:240-253
    DOI: 10.1080/07350015.2015.1026774
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/07350015.2015.1026774
    Download Restriction: Access to full text is restricted to subscribers.

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

    Other versions of this item:

    References listed on IDEAS

    as
    1. Cheung, Yin-Wong & Ng, Lilian K., 1996. "A causality-in-variance test and its application to financial market prices," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 33-48.
    2. King, Mervyn A & Wadhwani, Sushil, 1990. "Transmission of Volatility between Stock Markets," Review of Financial Studies, Society for Financial Studies, vol. 3(1), pages 5-33.
    3. Su, Liangjun & White, Halbert, 2007. "A consistent characteristic function-based test for conditional independence," Journal of Econometrics, Elsevier, vol. 141(2), pages 807-834, December.
    4. Kristin J. Forbes & Roberto Rigobon, 2002. "No Contagion, Only Interdependence: Measuring Stock Market Comovements," Journal of Finance, American Finance Association, vol. 57(5), pages 2223-2261, October.
    5. Sims, Christopher A, 1980. "Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered," American Economic Review, American Economic Association, vol. 70(2), pages 250-257, May.
    6. Bodart, Vincent & Candelon, Bertrand, 2009. "Evidence of interdependence and contagion using a frequency domain framework," Emerging Markets Review, Elsevier, vol. 10(2), pages 140-150, June.
    7. Angelos Kanas, 2002. "Mean and Variance Causality between Official and Parallel Currency Markets: Evidence from Four Latin American Countries," The Financial Review, Eastern Finance Association, vol. 37(2), pages 137-163, May.
    8. Su, Liangjun & White, Halbert, 2008. "A Nonparametric Hellinger Metric Test For Conditional Independence," Econometric Theory, Cambridge University Press, vol. 24(04), pages 829-864, August.
    9. repec:taf:jnlbes:v:30:y:2012:i:2:p:275-287 is not listed on IDEAS
    10. Candelon, Bertrand & Jo√ęts, Marc & Tokpavi, Sessi, 2013. "Testing for Granger causality in distribution tails: An application to oil markets integration," Economic Modelling, Elsevier, vol. 31(C), pages 276-285.
    11. Granger, C. W. J., 1980. "Testing for causality : A personal viewpoint," Journal of Economic Dynamics and Control, Elsevier, vol. 2(1), pages 329-352, May.
    12. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    13. Hong, Yongmiao & Liu, Yanhui & Wang, Shouyang, 2009. "Granger causality in risk and detection of extreme risk spillover between financial markets," Journal of Econometrics, Elsevier, vol. 150(2), pages 271-287, June.
    14. Chafik Bouhaddioui & Roch Roy, 2006. "A Generalized Portmanteau Test For Independence Of Two Infinite-Order Vector Autoregressive Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(4), pages 505-544, July.
    Full references (including those not matched with items on IDEAS)

    Citations

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


    Cited by:

    1. repec:ipg:wpaper:2014-538 is not listed on IDEAS
    2. repec:ipg:wpaper:2014-468 is not listed on IDEAS
    3. repec:ipg:wpaper:2014-530 is not listed on IDEAS
    4. repec:ipg:wpaper:2014-559 is not listed on IDEAS
    5. repec:ipg:wpaper:2014-526 is not listed on IDEAS
    6. repec:ipg:wpaper:2014-604 is not listed on IDEAS
    7. repec:ipg:wpaper:2014-534 is not listed on IDEAS
    8. repec:ipg:wpaper:2014-511 is not listed on IDEAS
    9. repec:ipg:wpaper:2014-551 is not listed on IDEAS
    10. repec:ipg:wpaper:2014-500 is not listed on IDEAS
    11. repec:ipg:wpaper:2014-493 is not listed on IDEAS
    12. repec:ipg:wpaper:2014-531 is not listed on IDEAS
    13. repec:ipg:wpaper:2014-571 is not listed on IDEAS
    14. repec:ipg:wpaper:2014-521 is not listed on IDEAS
    15. repec:ipg:wpaper:2014-451 is not listed on IDEAS
    16. repec:ipg:wpaper:2014-510 is not listed on IDEAS
    17. repec:ipg:wpaper:2014-466 is not listed on IDEAS

    More about this item

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

    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:jnlbes:v:34:y:2016:i:2:p:240-253. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Chris Longhurst). General contact details of provider: http://www.tandfonline.com/UBES20 .

    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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.