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

By B. Candelon, Sessi Tokpavi. 2014

89 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/c63a00ee0bffd9ab059778bc06b503b966934762

How researchers see this paper

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In Economics and Econometrics

1. Barbara Casu
2. Fotios Pasiouras
3. Donsyah Yudistira

In Finance

1. Barbara Casu
2. Fotios Pasiouras
3. Rima Turk Ariss
4. Donsyah Yudistira

Citations in the corpus

• Risk spillovers between FinTech and traditional financial institutions: Evidence from the U.S. (2020, methodology): “We then construct three types of spillover networks (downside-to-downside, upside-to-upside, and center-to-center) and estimate pairwise risk spillovers by using the Granger causality test across quantiles (Candelon and Tokpavi, 2016).”