This paper explores the performance of a global minimum variance (GMV) portfolio in dependence of the structure of the covariance matrix and the type of volatility model. We investigate quantitative portfolio strategies based on patterns of the covariance matrix, especially a diagonal covariance...

We illustrate in this paper the use of multivariate time series forecasts for portfolio construction and address the following research questions: First, how can forecasts of time series models be used for portfolio weight selection? Second, what kind of time series information improves...

This paper compares different models for volatility forecasts with respect to the value at risk performance (VaR) for daily stock index returns. The VaR measures the potential loss of a portfolio for the next period at a given significance level. We will focus on the question if the choice of...

We suggest a new class of cross-sectional space-time models based on local AR models and nearest neighbors using distances between observations. For the estimation we use a tightness prior for prediction of regional GDP forecasts. We extend the model to the model with exogenous variable model...

In this paper we describe the EAR (regional economic accessibility) model to investigate the impact of the improvement of railroad infrastructure on regional GDP, population and firms growth in 99 Austrian regions. We evaluate the impact of four potential railroad infrastructure investment...

The extended Hodrick-Prescott (HP) method was developed by Polasek (2011) for a class of data smoother based on second order smoothness. This paper develops a new extended HP smoothing model that can be applied for spatial smoothing problems. In Bayesian smoothing we need a linear regression...