One of the major motivations for the analysis and modeling of time series data is the forecasting of future outcomes. The use of interval forecasts instead of point forecasts allows us to incorporate the apparent forecast uncertainty. When forecasting count time series, one also has to account...

We propose an averaging rule that combines established minimum-variance strategies to minimize the expected out-of-sample variance. Our rule overcomes the problem of selecting the “best” strategy ex-ante and diversifies remaining estimation errors of the single strategies included in the...

WThis doctoral thesis focuses on the effects of investor sentiment on asset pricing and the challenges of portfolio optimization under parameter uncertainty. The first essay "Sentiment risk premia in the cross-section of global equity" applies a recently developed sentiment proxy to the...

This thesis analyses whether considering ambiguity aversion in portfolio optimization improves the out-of-sample performance of portfolio optimization approaches. Furthermore, it is assessed which role ambiguity aversion plays in improving the portfolio performance, especially compared with the...

In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is...

In forecasting count processes, practitioners often ignore the discreteness of counts and compute forecasts based on Gaussian approximations instead. For both central and non-central point forecasts, and for various types of count processes, the performance of such approximate point forecasts is...