A Non-parametric Test for Granger-Causality in Distribution with Application to Financial Contagion

Bertrand Candelon*, S. Tokpavi*

*Corresponding author for this work

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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.

Original languageEnglish
Pages (from-to)240 - 253
Number of pages14
JournalJournal of Business & Economic Statistics
Volume34
Issue number2
DOIs
Publication statusPublished - 2 Apr 2016

Keywords

  • Kernel-based test
  • Tails
  • Financial spillover
  • CONDITIONAL-INDEPENDENCE
  • TIME-SERIES
  • MARKETS
  • RISK

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