Testing for Causality in Variance Usinf 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 log-likelihood function. Both underestimation of the BEKK order and neglecting a potential leverage effect in volatility involve minor losses but retain the power advantage in comparison with residual based testing.
Year of publication: |
2008
|
---|---|
Authors: | HAFNER, Christian M. ; HERWARTZ, Helmut |
Published in: |
Annales d'Economie et de Statistique. - École Nationale de la Statistique et de l'Admnistration Économique (ENSAE). - 2008, 89, p. 215-241
|
Publisher: |
École Nationale de la Statistique et de l'Admnistration Économique (ENSAE) |
Saved in:
Online Resource
Saved in favorites
Similar items by person
-
Testing for Causality in Variance using Multivariate GARCH Models
Hafner, Christian M., (2004)
-
Hafner, Christian M., (1999)
-
Time-varying market price of risk in the CAPM: Approaches, empirical evidence and implications
Hafner, Christian M., (1999)
- More ...