An inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models is that they measure only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the performance of...

The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter ë = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some...

We tackle the nowcasting problem at the regional level using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly use the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each...

The ‘saving for a rainy day’ hypothesis implies that households’ saving decisions reflect that they can (rationally) predict future income declines. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers and it holds under the null that the...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

The ‘saving for a rainy day' hypothesis implies that households' saving decisions reflect that they can (rationally) predict future income declines. The empirical relevance of this hypothesis plays a key role in discussions of fiscal policy multipliers and it holds under the null that the...

We tackle the nowcasting problem at the regional level using a large set of indicators (regional, national and international) for the years 1998 to 2013. We explicitly use the ragged-edge data structure and consider the different information sets faced by a regional forecaster within each...

We introduce a new hybrid approach to joint estimation of Value at Risk (VaR) and Expected Shortfall (ES) for high quantiles of return distributions. We investigate the relative performance of VaR and ES models using daily returns for sixteen stock market indices (eight from developed and eight...

An inherent problem with comparing and ranking competing Value at Risk (VaR) and Expected shortfall (ES) models is that they measure only a single realization of the underlying data generation process. The question is whether there is any significant statistical difference in the performance of...

The HP filter is the most popular filter for extracting the trend and cycle components from an observed time series. Many researchers consider the smoothing parameter λ = 1600 as something like an universal constant. It is well known that the HP filter is an optimal filter under some...