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A Nonparametric Test for Granger-causality in Distribution with Application to Financial Contagion

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  • Bertrand Caudelon
  • Sessi Tokpavi

Abstract

This paper 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 introduced by Hong et al. (2009) and hence shares its main advantage, by checking a large number of lags with higher order lags discounted. Besides, our test is highly exible as it can be used to check for Granger-causality in specific regions on the distribution supports, like the center or the tails. We prove that it converges asymptotically to a standard Gaussian distribution under the null hypothesis and thus it 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 for a set of European stock markets in order to analyse the spill-overs during the recent European crisis and to distinguish contagion from interdependence effect.

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Bibliographic Info

Paper provided by University of Paris West - Nanterre la Défense, EconomiX in its series EconomiX Working Papers with number 2014-18.

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Length: 42 pages
Date of creation: 2014
Date of revision:
Handle: RePEc:drm:wpaper:2014-18

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Keywords: Granger-causality; Distribution; Tails; Kernel-based test; Financial Spill-over.;

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  1. 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, 05.
  2. 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.
  3. 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.
  4. 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.
  5. Bodart,Vincent & Candelon,Bertrand, 2005. "Evidences of Interdependence and Contagion using a Frequency Domain Framework," Research Memorandum 024, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
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  7. 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.
  8. Kristin Forbes & Roberto Rigobon, 1999. "No Contagion, Only Interdependence: Measuring Stock Market Co-movements," NBER Working Papers 7267, National Bureau of Economic Research, Inc.
  9. Sims, Christopher A, 1980. "Comparison of Interwar and Postwar Business Cycles: Monetarism Reconsidered," American Economic Review, American Economic Association, vol. 70(2), pages 250-57, May.
  10. repec:taf:jnlbes:v:30:y:2012:i:2:p:275-287 is not listed on IDEAS
  11. 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.
  12. Candelon, Bertrand & Joëts, 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.
  13. 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, 07.
  14. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-38, July.
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