A two-stage forecasting approach for long memory time series is introduced. In the first step we estimate the … and yields good forecasting results. …

We propose a new family of easy-to-implement realized volatility based forecasting models. The models exploit the …

, forecasting of the full density for long horizons is feasible, which we pursue. We document variability in conditional variances … over time, which stresses the importance of careful modeling and forecasting of volatility. We show that improved model fit …

This paper applies three universal approximators for forecasting. They are the Artificial Neural Networks, the …

We propose a parametric state space model with accompanying estimation and forecasting framework that combines long … process, the model consistently belongs to the 10% Model Confidence Set when considering out-of-sample forecasting performance … as the only one among four competing dynamic models for all forecasting horizons when applied to high frequency stock …

versa occurs over time. Such a change has important implications for forecasting, as negligence may lead to inaccurate model …. Seven different forecasting strategies based on a biasedcorrected estimator are compared by means of a large-scale Monte … predictive ability and its balanced performance among different settings strongly advocate the use of forecasting strategies …

The use of large-dimensional factor models in forecasting has received much attention in the literature with the … model which is better suited for forecasting compared to the traditional principal components (PC) approach.We provide an … asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic …