In this paper, we apply machine learning to forecast the conditional variance of long-term stock returns measured in excess of different benchmarks, considering the short- and long-term interest rate, the earnings-by-price ratio, and the inflation rate. In particular, we apply in a two-step...

The increasing availability of geospatial data (i.e., exact longitudes and latitudes for each house) has the potential to improve the quality of house price indexes. It is not clear though how best to use this information. We show how geospatial data can be included as a nonparametric spline...

Among many developments in statistical modelling in recent years, non- and semiparametric methods have proved to be a particularly powerful data-analytic tool. Nevertheless, there still exist justified doubts regarding there forecasting performance, for example in the context of financial time...