A causality-in-variance test and its application to financial market prices

This paper develops a test for causality in variance. The test is based on the residual cross-correlation function (CCF) and is robust to distributional assumptions. Asymptotic normal and asymptotic chi(2) statistics are derived under the null hypothesis of no causality in variance. Monte Carlo results indicate that the proposed CCF lest has good empirical size and power properties. Two empirical examples illustrate that the causality test yields useful information on the temporal dynamics and the interaction between two time series.