We consider two multivariate long-memory ARCH models, which extend the univariate long-memory ARCH models, we first consider a long-memory extension of the restricted constant conditional correlations (CCC) model introduced by Bollerslev (1990), and we propose a new unrestricted conditional...

Multivariate Volatility Models belong to the class of nonlinear models for financial data. Here we want to focus on multivariate GARCH models. These models assume that the variance of the innovation distribution follows a time dependent process conditional on information which is generated by...

For univariate time series we suggest a new variant of efficient score tests against fractional alternatives. This test has three important merits. First, by means of simulations we observe that it is superior in terms of size and power in some situations of practical interest. Second, it is...

Correspondence analysis (CA) is a descriptive method which allows us to analyze and to XploRe the structure of contingency tables (or, by extension, non-negative tables where rows and columns are the entities of interest). It is similar to principal cornponent analysis (PCA) in the sense that,...

The small sample properties of two types of Chow tests are investigated in the context of multiple time series models. It is found that the tests may have substantially distorted size if the sample size is not large relative to the number of parameters in the model under study. In particular the...

This paper derives conditions for the existence of fourth moments of multivariate GARCH processes in the general vector specification and gives explicit results for the fourth moments and autocovariances of the squares and cross-products. Results are provided for the kurtosis and co-kurtosis...