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 … univariate GARCH specifications where cross sectional dependecies are ignored. Moreover, to illustrate the scope of the bivariate …

We establish a relation between stochastic volatility models and the class of generalized hyperbolic distributions … background of hyperbolic distributions and prove stationary distributions of certain GARCH-type models to be generalized …

prices caused by stochastic volatility. -- option pricing ; autoregression ; heteroskedasticity ; GARCH ; leverage effect …

In this paper we study new nonlinear GARCH models mainly designed for time series with highly persistent volatility …. For such series, conventional GARCH models have often proved unsatisfactory because they tend to exaggerate volatility … transition GARCH models except for the novel feature that a lagged value of conditional variance is used as the transition …

This paper offers a new approach for estimation and forecasting of the volatility of financial time series. No … assumption is made about the parametric form of the processes, on the contrary we only suppose that the volatility can be … homogeneity, then the estimate of the volatility can be simply obtained by local averaging. We construct a locally adaptive …

Stochastic Volatility (SV) models are widely used in financial applications. To decide whether standard parametric …

is the volatility coefficient which in turn obeys an autoregression type equation log v t = w + a S t- l + nt with an …