Testing for causality in variance using multivariate GARCH models

Christian Hafner and H. Herwartz

No EI 2004-20, Econometric Institute Research Papers from Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute

Abstract: 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.

Keywords: causality; local power; multivariate volatility (search for similar items in EconPapers)
JEL-codes: C22 C52 (search for similar items in EconPapers)
Date: 2004-05-21
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Citations: View citations in EconPapers (15)

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Related works:
Journal Article: Testing for Causality in Variance Usinf Multivariate GARCH Models (2008)
Working Paper: Testing for Causality in Variance using Multivariate GARCH Models (2004)
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Persistent link: https://EconPapers.repec.org/RePEc:ems:eureir:1285

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