We investigate the information content of stock correlation based network measures for systemic risk rankings, such as SIFIRank (based on Google's PageRank). Using European banking data, we first show that SIFIRank is empirically equivalent to a ranking based on average pairwise stock...

We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...

Accurate prediction of the frequency of extreme events is of primary importance in many financialapplications such as Value-at-Risk (VaR) analysis. We propose a semi-parametric method for VaRevaluation. The largest risks are modelled parametrically, while smaller risks are captured by the...

We analyze how adding the shadow economy to official output figures affects technical efficiency. We find that this only slightly affects the ranking of efficiency scores, but increases average efficiency. Our results are robust to the functional form of the production technology and the...

This paper analyzes the influence of the shadow economy on corruption and vice versa. We hypothesize that corruption and shadow economy are substitutes in high income countries while they are complements in low income countries. The hypotheses are tested for a crosssection of 120 countries and a...