University of Passau

About: University of Passau is a education organization based out in Passau, Bayern, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 1543 authors who have published 4763 publications receiving 93338 citations.

Analyzing the interest rate risk of banks using time series of accounting-based data: evidence from Germany

Abstract: This paper describes the first thorough analysis of the interest risk of German banks on an individual bank level. We develop a new method that is based on time series of accountingbased data to quantify the interest risk of banks and apply it to analyze the German banking system. We find evidence that our model yields a significantly better fit of banks' internally quantified interest rate risk than a standard approach that relies on one-point-in-time data, and that the interest rate risk differs between banks of different size and banking group. Additionally, we find structural differences between trading book and non-trading book institutions.

The shape of feature code: an analysis of twenty C-preprocessor-based systems

Abstract: Feature annotations (e.g., code fragments guarded by #ifdef C-preprocessor directives) control code extensions related to features. Feature annotations have long been said to be undesirable. When maintaining features that control many annotations, there is a high risk of ripple effects. Also, excessive use of feature annotations leads to code clutter, hinder program comprehension and harden maintenance. To prevent such problems, developers should monitor the use of feature annotations, for example, by setting acceptable thresholds. Interestingly, little is known about how to extract thresholds in practice, and which values are representative for feature-related metrics. To address this issue, we analyze the statistical distribution of three feature-related metrics collected from a corpus of 20 well-known and long-lived C-preprocessor-based systems from different domains. We consider three metrics: scattering degree of feature constants, tangling degree of feature expressions, and nesting depth of preprocessor annotations. Our findings show that feature scattering is highly skewed; in 14 systems (70 %), the scattering distributions match a power law, making averages and standard deviations unreliable limits. Regarding tangling and nesting, the values tend to follow a uniform distribution; although outliers exist, they have little impact on the mean, suggesting that central statistics measures are reliable thresholds for tangling and nesting. Following our findings, we then propose thresholds from our benchmark data, as a basis for further investigations.

Voting on redistribution with tax evasion

Abstract: This paper analyzes voting on a linear income tax whose proceeds are redistributed lump sum to the taxpayers. Individuals can evade taxes, which leads to penalties if evasion is detected. Since preferences satisfy neither single peakedness nor single crossing, a voting equilibrium may not exist. When an equilibrium does exist, there are several possible outcomes. There may be ‘conventional’ equilibria where the rich are expropriated by the poor and middle class. There may be equilibria without full expropriation where redistribution is limited by the threat of evasion. Finally, there may be equilibria where redistribution goes from the middle class to the rich and poor.