A Nonparametric Test of Granger Causality in Continuous Time

Abstract:

In this talk, I consider a nonparametric Granger causality test for continuous time point process data. Unlike popular Granger causality tests with strong parametric assumptions on discrete time series, the test applies directly to strictly increasing raw event time sequences sampled from a bivariate temporal point process satisfying mild stationarity and moment conditions. This eliminates the sensitivity of the test to model assumptions and data sampling frequency. Taking the form of an L2-norm, the test statistic delivers a consistent test against all alternatives with pairwise causal feedback from one component process to another, and can simultaneously detect multiple causal relationships over variable time spans up to the sample length. The test enjoys asymptotic normality under the null of no Granger causality and exhibits reasonable empirical size and power performance. Its usefulness is illustrated in three applications: (i) credit contagion of U.S. corporate bankruptcies over different industrial sectors, (ii) financial contagion across international stock exchanges, and (iii) market microstructure analysis of trade and quote causal dynamics.