Testing for causality in variance using multivariate GARCH models
Tests of causality in variance in multiple time series have been proposed recently, based on residuals of estimated univariate models. Although such tests are applied frequently little is known about their power properties. In this paper we show that a convenient alternative to residual based testing is to specify a multivariate volatility model, such as multivariate GARCH (or BEKK), and construct a Wald test on noncausality in variance. We compare both approaches to testing causality in variance in terms of asymptotic and finite sample properties. The Wald test is shown to have superior power properties under a sequence of local alternatives. Furthermore, we show by simulation that the Wald test is quite robust to misspecification of the order of the BEKK model, but that empirical power decreases substantially when asymmetries in volatility are ignored.
|causality, local power, multivariate volatility|
|Time-Series Models; Dynamic Quantile Regressions (jel C22), Model Evaluation and Testing (jel C52)|
|Econometric Institute Research Papers|
|Organisation||Erasmus School of Economics|
Hafner, C.M, & Herwartz, H. (2004). Testing for causality in variance using multivariate GARCH models (No. EI 2004-20). Econometric Institute Research Papers. Retrieved from http://hdl.handle.net/1765/1285