There is increasing demand for models of time-varying and non-Gaussian dependencies for mul- tivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical...

There is increasing demand for models of time-varying and non-Gaussian dependencies for mul- tivariate time-series. Available models suffer from the curse of dimensionality or restrictive assumptions on the parameters and the distribution. A promising class of models are the hierarchical...

We consider estimation and inference in fractionally integrated time series models driven by shocks which can display conditional and unconditional heteroskedasticity of unknown form. Although the standard conditional sum-of-squares (CSS) estimator remains consistent and asymptotically normal in...

To accommodate the inhomogenous character of financial time series over longer time periods, standard parametric models can be extended by allow- ing their coeffcients to vary over time. Focusing on conditional heteroscedas- ticity models, we discuss various strategies to identify and estimate...

The increasing works on parameter instability, structural changes and regime switches lead to the natural research question whether the assumption of stationarity is appropriate to model volatility processes. Early econometric studies have provided testing procedures of covariance stationarity...

An adaptive estimator is an efficient estimator for a model that is only partially specified.

Measuring dependence in a multivariate time series is tantamount to modelling its dynamic structure in space and time. In the context of a multivariate normally distributed time series, the evolution of the covariance (or correlation) matrix over time describes this dynamic. A wide variety of...

For a Lévy process X having finite variation on compact sets and finite first moments, u (dx) = xv (dx) is a finite signed measure which completely describes the jump dynamics. We construct kernel estimators for linear functionals of u and provide rates of convergence under regularity...