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

This paper introduces a kernel-based non-parametric inferential pro-cedure to test for Granger causality in distribution. This test is a multi-variate extension of the kernel-based Granger causality test in tail event introduced by Hong, Liu, and Wang (2009). 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 asymptoti-cally 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 e↵ects