GARCH Models have become a workhouse in volatility forecasting of financial and monetary market time series. In this article, we assess the small sample properties in estimation and the performance in volatility forecasting of four competing distribution free methods, including quasi-maximum...

We analyze around 200 different financial time series, i.e. components of Dow Jones, Nasdaq, FTSE and Nikkei with seven different VaR approaches. We differentiate our analysis according to characteristics that can be observed. Our analysis shows that in high risk situations in which the time...

Ab dem Berichtsjahr 2023 wird für die amtliche Flächenstatistik ein überarbeiteter Nutzungsartenkatalog zum Einsatz kommen. Anlass hierfür ist ein in der Vermessungs­- und Katasterverwaltung durchgeführter und von der Statistik begleiteter Paradigmenwechsel, dem eine neue Sicht auf die...

We analyze around 200 different financial time series, i.e. components of Dow Jones, Nasdaq, FTSE and Nikkei with seven different VaR approaches. We differentiate our analysis according to characteristics that can be observed. Our analysis shows that in high risk situations in which the time...

GARCH Models have become a workhouse in volatility forecasting of financial and monetary market time series. In this article, we assess the small sample properties in estimation and the performance in volatility forecasting of four competing distribution free methods, including quasi-maximum...

GARCH Models have become a workhouse in volatility forecasting of financial and monetary market time series. In this article, we assess the small sample properties in estimation and the performance in volatility forecasting of four competing distribution free methods, including quasi-maximum...

We analyze around 200 different financial time series, i.e. components of Dow Jones, Nasdaq, FTSE and Nikkei with seven different VaR approaches. We differentiate our analysis according to characteristics that can be observed. Our analysis shows that in high risk situations in which the time...