This paper introduces a model framework for dynamic credit rating processes. Our framework aggregates ordinal rating information stemming from a variety of rating sources. The dynamic of the consensus rating captures systematic as well as idiosyncratic changes. In addition, our framework allows to validate the different rating sources by analyzing the mean/variance structure of the rating errors. In an empirical study for the iTraxx Europe companies rated by the big three external rating agencies we use Bayesian techniques to estimate the consensus ratings for these companies. The advantages are illustrated by comparing our dynamic rating model to a benchmark model. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

Measuring the risk of a credit portfolio is a challenge for financial institutions because of the regulations brought by the Basel Committee. In recent years lots of models and state-of-the-art methods, which utilize Monte Carlo simulation, were proposed to solve this problem. In most of the models factors are used to account for the correlations between obligors. We concentrate on the the normal copula model, which assumes multivariate normality of the factors. Computation of value at risk (VaR) and expected shortfall (ES) for realistic credit portfolio models is subtle, since, (i) there is dependency throughout the portfolio; (ii) an efficient method is required to compute tail loss probabilities and conditional expectations at multiple points simultaneously. This is why Monte Carlo simulation must be improved by variance reduction techniques such as importance sampling (IS). Thus a new method is developed for simulating tail loss probabilities and conditional expectations for a standard credit risk portfolio. The new method is an integration of IS with inner replications using geometric shortcut for dependent obligors in a normal copula framework. Numerical results show that the new method is better than naive simulation for computing tail loss probabilities and conditional expectations at a single x and VaR value. Finally, it is shown that compared to the standard t statistic a skewness-correction method of Peter Hall is a simple and more accurate alternative for constructing confidence intervals. (author´s abstract) / Series: Research Report Series / Department of Statistics and Mathematics

The paper is concerned with counterparty credit risk management for credit default swaps in the presence of default contagion. In particular, we study the impact of default contagion on credit value adjustments such as the BCCVA (Bilateral Collateralized Credit Value Adjustment) of Brigo et al. 2012 and on the performance of various collateralization strategies. We use the incomplete-information model of Frey and Schmidt (2012) as vehicle for our analysis. We find that taking contagion effects into account is important for the effectiveness of the strategy and we derive refined collateralization strategies to account for contagion effects. (authors' abstract) / Series: Research Report Series / Department of Statistics and Mathematics

This paper explores the nexus between uncertainty and credit restrictions. A Post Keynesian approach to an explanation of access rationing to credit is developed and contrasted with the dominant relationship lending school. It is argued that access rationing to credit has be understood in terms of uncertainty and power. Differences in systemic uncertainty to which hetrogenous market participants are exposed can explain the reluctance of banks to lend to certain applicants. Monopsonistic power and uncertainty further help to understand why banks of a different size show differences in their lending behavior. (author's abstract) / Series: Department of Economics Working Paper Series

Der Aufstieg des maschinellen Lernens (ML) und die rasante Digitalisierung der Wirtschaft haben die Entscheidungsprozesse in der Finanzbranche erheblich verändert. Finanzinstitute setzen zunehmend auf ML, um die Entscheidungsfindung zu unterstützen. Kreditscoring ist eine der wichtigsten ML-Anwendungen im Finanzbereich. Die Aufgabe von Kreditscoring ist die Unterscheidung ob ein Antragsteller einen Kredit zurückzahlen wird. Finanzinstitute verwenden ML, um Scorecards zu entwickeln, die die Ausfallwahrscheinlichkeit eines Kreditnehmers einschätzen und Genehmigungsentscheidungen automatisieren. Diese Dissertation konzentriert sich auf drei große Herausforderungen, die mit dem Aufbau von ML-basierten Scorekarten für die Bewertung von Verbraucherkrediten verbunden sind: (i) Optimierung von Datenerfassungs- und -speicherkosten bei hochdimensionalen Daten von Kreditantragstellern; (ii) Bewältigung der negativen Auswirkungen von Stichprobenverzerrungen auf das Training und die Bewertung von Scorekarten; (iii) Messung und Sicherstellung der Fairness von Instrumenten bei gleichzeitig hoher Rentabilität. Die Arbeit bietet und testet eine Reihe von Instrumenten, um jede dieser Herausforderungen zu lösen und die Entscheidungsfindung in Finanzinstituten zu verbessern. Erstens entwickeln wir Strategien zur Auswahl von Merkmalen, die mehrere unternehmensbezogene Zielfunktionen optimieren. Unsere Vorschläge reduzieren die Kosten der Datenerfassung und verbessern die Rentabilität der Modelle. Zweitens schlagen wir Methoden zur Abschwächung der negativen Auswirkungen von Stichprobenverzerrungen vor. Unsere Vorschläge gleichen die Verluste aufgrund von Verzerrungen teilweise aus und liefern zuverlässigere Schätzungen der künftigen Scorecard-Leistung. Drittens untersucht die Arbeit faire ML-Praktiken in Kreditscoring. Wir katalogisieren geeignete algorithmische Optionen für die Einbeziehung von Fairness-Zielen und verdeutlichen den Kompromiss zwischen Gewinn und Fairness. / The rise of machine learning (ML) and the rapid digitization of the economy has substantially changed decision processes in the financial industry. Financial institutions increasingly rely on ML to support decision-making. Credit scoring is one of the prominent ML applications in finance. The task of credit scoring is to distinguish between applicants who will pay back the loan or default. Financial institutions use ML to develop scoring models to estimate a borrower's probability of default and automate approval decisions. This dissertation focuses on three major challenges associated with building ML-based scorecards in consumer credit scoring: (i) optimizing data acquisition and storage costs when dealing with high-dimensional data of loan applicants; (ii) addressing the adverse effects of sampling bias on training and evaluation of scoring models; (iii) measuring and ensuring the scorecard fairness while maintaining high profitability. The thesis offers a set of tools to remedy each of these challenges and improve decision-making practices in financial institutions. First, we develop feature selection strategies that optimize multiple business-inspired objectives. Our propositions reduce data acquisition costs and improve model profitability and interpretability. Second, the thesis illustrates the adverse effects of sampling bias on model training and evaluation and suggests novel bias correction frameworks. The proposed methods partly recover the loss due to bias, provide more reliable estimates of the future scorecard performance and increase the resulting model profitability. Third, the thesis investigates fair ML practices in consumer credit scoring. We catalog algorithmic options for incorporating fairness goals in the model development pipeline and perform empirical experiments to clarify the profit-fairness trade-off in lending decisions and identify suitable options to implement fair credit scoring and measure the scorecard fairness.

Diese Dissertation besteht aus drei Kapiteln, die durch die europäische Schuldenkrise als gemeinsames Thema verbunden sind. Kapitel eins untersucht die Auswirkungen der Finanzintegration auf das Kreditangebot der Banken und die Realwirtschaft. Im Jahr 2007 ersetzt die Europäische Zentralbank die nationalen Sicherheitenlisten durch eine einzige Euroraumliste. Für Banken mit solch neu zugelassene Sicherheiten sinken die Finanzierungskosten. Diese Banken vergeben mehr Kredite, insbesondere an risikoreichere und unproduktivere Firmen in anderen Euroraumländern. Bei diesen Firmen wiederum nehmen Beschäftigung und Investitionen zu. Die Ergebnisse verdeutlichen die unbeabsichtigte Rolle der Finanzintegration beim Anheizen grenzüberschreitender Kreditblasen. Kapitel zwei untersucht die politischen Verbindungen von Bankvorständen in Krisenzeiten. Regierungen beeinflussen nach einer staatlichen Bankenrettung die Zusammensetzung von Bankvorständen, um sich Kontrollrechte zu sichern. Wir stellen fest, dass die Anzahl der politischen Vorstandsmitglieder nach einer staatlichen Unterstützung um 21,4% steigt. Gerettete Banken mit solch neuen politischen Vorständen schneiden in Bezug auf Marktkapitalisierung und Bewertung deutlich besser ab als gerettete Banken ohne solche Verbindungen. Kapitel drei liefert kausale Belege für die Auswirkungen von Kreditklemmen auf politische Radikalisierung. Mit Daten zu Bank-Firmen-Verbindungen und kommunalen Wahlergebnissen zeigen wir, dass Unternehmen mit einer Beziehung zu schwachen Banken einen Rückgang ihres Kreditangebots und des Beschäftigungswachstums erleben. Anschließend schätzen wir die Auswirkungen der Arbeitslosigkeit auf das Wahlverhalten. Wir konstruieren ein Instrument für die Arbeitslosigkeit, das auf der Abhängigkeit gegenüber schwachen ausländischen Banken auf kommunaler Ebene basiert. Ein Anstieg der instrumentierten Arbeitslosigkeit führt zu einer Steigerung der Wählerradikalisierung um 7 Prozentpunkte. / This thesis consists of three chapters linked by the European Debt Crisis as their common theme. Chapter One studies the effect of financial integration on bank credit supply and the real economy. In 2007, the European Central Bank replaces national collateral lists with a single euro area list. Banks holding newly eligible assets experience a reduction in their cost of funding.These banks lend more, especially to riskier and less productive borrowers located in other euro area countries. The borrowers in turn experience growth in employment and investment. The results highlight the unintended role of financial integration in fueling crossborder credit booms. Chapter Two investigates the political ties of too-big-to-fail bank boards in crisis times. After a bailout, governments are likely to influence bank board compositions to secure control rights. Combining two novel datasets on political ties of banks and state aid in the European Union, we find that the number of politically connected board members increases by 21.4% following government support. Bailed-out banks with such new political ties perform better in terms of market capitalisation and valuation than bailed-out banks without such ties. Chapter Three provides causal evidence on the effect of credit crunches on political radicalisation. We combine data on bank-firm connections and electoral outcomes at the city-level during the 2008-2014 Spanish Financial Crisis. First, we show that firms in a relationship with weak banks experience a reduction in their loan supply and employment growth. Next, we estimate the effects of unemployment on voting behaviour. We construct an instrument for unemployment based on the city-level exposure to foreign weak banks. We find that a one standard deviation increase in instrumented unemployment translates into a 7 percentage point increase in the radicalisation of voters.