The estimation of multivariate GARCH models remains a challenging task, even in modern computer environments. This manuscript shows how Independent Component Analysiscan be used to estimate the Generalized Orthogonal GARCH model in a fraction of the time otherwise required. The proposed method...

World power and gas markets have a natural relationship with global tradable carbon permits markets, including the U.S. Clean Air Act Amendments and the EU Emissions Trading Scheme, the latter officially launched in January 2005. Electric utilities operate their power plants based in part on the...

A resampling method based on the bootstrap and a bias-correction step is developed for improving the Value-at-Risk (VaR) forecasting ability of the normal GARCH model. Compared to the use of more sophisticated GARCH models, the new method is fast, easy to implement, numerically reliable, and,...

Closed-form, trivially evaluated approximations for the density and cumulative distribution function of the doubly noncentral t distribution are developed based on saddlepoint methods. They exhibit remarkable accuracy throughout the entire support of the distribution and are vastly superior to...

A new and quite general model class for modeling asset returns and forecasting Value at Risk is proposed. It combines a dynamic multi-component GARCH structure with the stable Paretian distributional assumption. The new model nests several successful models for modeling asset returns, including...

The recently proposed class of MixN-GARCH models, which couple a mixed normal distributional structure with linked GARCH-type dynamics, has been shown to offer a plausible decomposition of the contributions to volatility, as well as admirable out-of-sample forecasting performance, for financial...

This paper develops a general method for conducting exact small-sample inference in models which allow the estimator of the (scalar) parameter of interest to be expressed as the root of an estimating function, and which is particularly simple to implement for linear models with a covariance...