Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...

Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...

ARMA-GARCH-Modellierung; nicht-Normalität; normal-inverse Gauss-Verteilung (NIG-Verteilung); realisierte Momente; Staatsanleihen; Strom Forwards; stylized facts von Finanzzeitreihen; Value at Risk; Verteilung von Anleiherenditen

We investigate the combination of the dimension reduction methods SIR (Li, 1991) and DAME (Gather et al., 2001) with fuzzy-clustering to validate a given classification. We consider certain economic variables which are assumed to contain the information relevant to determine the current phase of...

The present method allows to detect outlying observations in data which may be described by a deterministic function plus a stochastic component. This type of functional relationship often occurs in experimental data, in toxicological research, for instance. The Hampel identifier, an outlier...

Methods of dimension reduction are very helpful and almost a necessity if we want to analyze high-dimensional time series since otherwise modelling affords many parameters because of interactions at various time-lags. We use a dynamic version of Sliced Inverse Regression (SIR; Li (1991)), which...

In investigations on the behaviour of robust estimators, typically their consistency and their asymptotic normality are studied as a necessity. Their rates of convergence, however, are often given less weight. We show here that the rate of convergence of a multivariate robust estimator to its...