A1172
Title: Testing for innovation symmetry in multivariate generalized autoregressive conditional heteroskedastic models
Authors: Wai-keung Li - The Education University of Hong Kong (Hong Kong) [presenting]
Abstract: Testing whether the innovation is symmetric or not is important for multivariate generalized autoregressive conditional heteroskedastic(GARCH) models. The traditional testing methods for univariate models depend on either smoothing or martingale transformation treatment, so their extension to multivariate models is not a straightforward or desirable task. A new consistent test is proposed to examine the symmetry of innovation in multivariate GARCH models using a characteristic measure. Regardless of the dimension of the multivariate GARCH model, the proposed test is easy-to-implement without involving any smoothing treatment. Under certain conditions, the asymptotic null distribution of the test is established. Surprisingly, it is found that the model estimation has a negligible effect on the asymptotic null distribution. Due to this important feature, a simple bootstrap method is provided to compute the critical values of the test. As an extension, similar testing methods for the general multivariate time series models are also applied in the presence of conditional mean.
