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Multivariate ordinal models in credit risk: Three essays
Get PDFThis dissertation deals with the development, implementation and application of a multivariate statistical framework for credit risk modeling, which is able to incorporate both, default (or failure) information and credit ratings.
Credit risk is the risk of a loss arising from a failure (or default) of a counterparty to meet its contractual obligations (e.g., McNeil et al., 2015). The modeling of credit risk in banks and insurance companies has received considerable attention from academics and practitioners over the last decades. From a regulatory point of view, the Basel Committee on Banking Supervision provides a sophisticated foundation for the assessment of credit risk (Basel I, 1988; Basel II, 2004; Basel III, 2011). According to this regulatory framework, credit risk management and the development of appropriate credit risk models have a crucial relevance for banks and insurance companies, influencing their capital requirements. The financial crisis of 2007-2009 has made the prediction of bankruptcies as well as the understanding of the drivers of creditworthiness an even more urgent matter. Credit rating agencies provide in their credit ratings a forward-looking opinion about the creditworthiness of firms and sovereigns. Even though external credit ratings from the big three players in the credit rating market (Standard and Poor’s (S&P), Moody’s and Fitch) where criticized in the aftermath of the financial crisis, they seem to remain the most common and widely used credit risk measure (Hilscher and Wilson, 2017). Alternatively to credit ratings, internal statistical models based on historical defaults, accounting and market information are often applied when modeling credit risk. Such internal credit risk models serve as a widely-used alternative to credit ratings. Among others Lipton et al. (2012) and Löffler (2013) argue that credit rating agencies react slowly to credit events and are outperformed by failure prediction models in terms of prediction accuracy. Nevertheless in scenarios where defaults are scarce credit ratings serve as an important measure of credit risk and present an alternative to statistical models.
The thesis consists of three research articles. The first paper is concerned with a multivariate extension of ordinal regression models. The model class of multivariate ordinal regression models is motivated by the fact that correlated ordinal data arises naturally when modeling credit ratings.
Existing model specifications are extended in several directions. E.g., we allow for a flexible covariate dependent correlation structure between the continuous variables underlying the ordinal credit ratings. Furthermore, in addition to an underlying multivariate normal distribution (multivariate probit link), a multivariate logistic distribution (multivariate logit link) is considered. Moreover, missing observations in the response variables can be dealt with by the model. An estimation algorithm based on composite maximum likelihood methods is implemented and the quality of the estimates is investigated by means of a comprehensive simulation study. The proposed model allows to obtain insights into the rating behaviour of the big three credit rating agencies.
The second research article aims at making the algorithm for the estimation of multivariate ordinal regression models developed in the first paper accessible for the statistical community. A flexible modeling framework for multiple ordinal measurements on the same subject is set up and
implemented in the form of an R package (R Core Team, 2019). The mvord package (Hirk et al., 2019b) is freely available on the “Comprehensive R Archive Network” (CRAN) and enhances the available statistical software for analyzing correlated ordinal data. The flexible and user-friendly model design allows practitioners and researchers, who deal with correlated ordinal data in various areas of application, for different error structures to capture the dependence among the multiple observations. In addition, flexible constraints on the regression coefficients and on the threshold
parameters can be set.
The third paper uses the framework developed and implemented in the first two research articles
to propose a novel multivariate credit risk model, where default or failure information together with rating or expert information are jointly modeled. The proposed credit risk model uses financial variables typically used for bankruptcy predictions to provide probabilities of default conditional on the credit ratings from one or more credit rating agencies. The model is able to account for
missing default and credit rating information. An empirical analysis on a data set of US firms over the period from 1985 to 2014 is conducted. Our findings suggest that the proposed joint modeling framework gives superior prediction accuracy and discriminatory power compared to state-of-the-art failure prediction models and shadow rating approaches
What do we know about poverty in North Korea?
Get PDFReliable quantitative information on the North Korean economy is extremely scarce. In particular, reliable income per capita and poverty figures for the country are not available. In this contribution, we provide for the first time estimates of absolute poverty rates in North Korean subnational regions based on the combination of innovative remote-sensednight-time light intensity data (monthly information for built areas) with estimated income distributions. Our results, which are robust to the use of different methods to approximatethe income distribution in the country, indicate that the share of persons living in extreme poverty in North Korea may be larger than previously thought. We estimate a poverty rate for the country of around 60% in 2018 and a high volatility in the dynamics of income at the national level in North Korea for the period 2012–2018. Income per capita estimates tend to decline significantly from 2012 to 2015 and present a recovery since 2016. The subnational estimates of income and poverty reveal a change in relative dynamics since the second half of the 2012–2018 period. The first part of the period is dominated by divergent dynamics inincome across regions, while the second half reveals convergence in regional income
Die Doctrine classique des Familienbonus+ - Eine Debatte in 5 Akten
Get PDFIm Rahmen dieses Beitrags werden die Auswirkungen der 2018 in Österreich eingeführten Familienförderungsmaßnahme Familienbonus+ in Hinblick auf die Ausschöpfung der steuerlichen Entlastung nach unterschiedlichen Faktoren, wie etwa Haushaltstruktur, Alter, Geschlecht, Bildung und Urbanisierungsgrad, analysiert. Die Simulation der Steuerreform wurde mithilfe des Mikrosimulationsmodells EUROMOD durchgeführt. Es zeigt sich, dass Familien bzw. Kinder in sehr unterschiedlichem Ausmaß von der Maßnahme profitieren, da manche Eltern über nicht genügend Einkommen verfügen oder zuvor mehr von den abgeschafften Steuerbegünstigungen (Kinderfreibetrag und Absetzbarkeit von Kinderbetreuungskosten) profitieren konnten. Entgegen der politischen Diskussion werden insbesondere Haushalte, in denen beide Elternteile Vollzeit arbeiten, nur unterproportional zu ihrer Steuerleistung entlastet, während Haushalte mit Vollzeit-Teilzeit-Aufteilung und AlleinverdienerInnen überproportional profitieren. Darüber hinaus werden insbesondere Personen zwischen 20 und 45 Jahren, welche in ländlichen Gebieten wohnen und einen Abschluss der Sekundarstufe aufweisen, entlastet. Zusätzlich zeigt sich, dass Männer aufgrund der hohen Einkommensdifferenzen zwischen den Geschlechtern über dreimal so viel von dieser Maßnahme profitieren wie Frauen.Series: INEQ Working Paper Serie
Austria in the COVID-19 Pandemic - Citizens' Satisfaction with Crisis Measures and Communication
Get PDFBackground: We assess satisfaction about various aspects of the 2020 COVID-19 crisis for a representative sample of 1798 respondents living in Austria. Survey questions were added to a previously planned data collection, based on concrete questions discussed at a BKA Clearing Board meeting (Tuesday, 14.04.2020: Subarbeitsgruppe Psycho-Soziale-Effekte im Rahmen von "COVID-19 / Future Operations").
Findings: Overall, people living in Austria are satisfied with the various crisis management elements of the COVID-19 pandemic, as answers are mainly at the positive side of the response scale that ranges from -3 (Very unsatisfied) to +3 (Very satisfied). Citizens are most satisfied with how well they implement the measures of the federal government themselves (and/or their employer) to overcome the Corona crisis, and about how they are able to comply with these measures. In contrast, they are least satisfied with how national media report on the measures (Newspapers, TV, etc.). Splitting-up satisfaction evaluations for gender, age, region, level of education, occupation, or sector of employment does show no or some small (but no substantial) differences for particular subgroups. We can observe an age effect for satisfaction on how others deal with the government's COVID-19 measures. This means: the older people are, the more satisfied they are about how others comply with the COVID-19 measures. Self-employed respondents are least satisfied with how the government is dealing with the crisis and communicating the measures. Students are most satisfied about that. However, it has to be noted that this data is from 17 April to 29 April (2020), which is just before loosening, in a second round, many of the restrictions on small businesses
Zum Altersvorsorgeverhalten in Österreich und in Japan - Ergebnisse einer vergleichenden empirischen Studie mit einer Unterscheidung nach städtischer und ländlicher Bevölkerung und Erhebungen in den jeweiligen Landessprachen (315 Probanden/-innen) einschließlich aller Datensätze / On Old-Age Provision Behaviour in Austria and in Japan - Results from a Comparative Empirical Study with a Distinction of Urban and Rural Population and Surveys in the Respective National Languages (315 Respondents) Including the Complete Database / Nr. 2 der „Wiener Beiträge zur Betriebswirtschaftlichen Sicherungswissenschaft“ (WrBtrgBwSichWiss)
Get PDFAnalytic Hierarchy Process for City Hub Location Selection - The Viennese Case
Get PDFGrowing urbanization and rising urban freight volumes contribute to increasing congestion, noise and pollution which negatively impact a city’s population. City hubs are one means of mitigating this problem by consolidating goods of different suppliers at the hub and cooperating in the last mile delivery. Because of the general shortage of urban space, a major challenge is finding an appropriate location for such a hub. This paper provides a decision support tool based on the analytic hierarchy process for the hub location selection problem, which considers quantitative and qualitative criteria. By involving three stakeholder groups – the municipality, logistics companies and citizens – the approach insures a comprehensive view. The application of the model is tested for the location selection of a midi-hub – a medium-sized city hub – in Vienna. Hence, our results show that a good compromise between different stakeholder views regarding a mid-hub location selection problem can be achieved by the application of our AHP-based decision support tool
'The economy' as if people mattered: Revisiting critiques of economic growth in a time of crisis
Get PDFCoronavirus (COVID-19) policy shut down the world economy with a range of government actions unprecedented outside of wartime. In this paper, economic systems dominated by a capital accumulating growth imperative are shown to have had their structural weaknesses exposed, revealing numerous problems including unstable supply chains, unjust social provisioning of essentials, profiteering, precarious employment, inequities and pollution. Such phenomena must be understood in the context of long standing critiques relating to the limits of economic systems, their consumerist values and divorce from biophysical reality. Critical reflection on the Coronavirus pandemic is combined with a review of how economists have defended economic growth as sustainable, Green and inclusive regardless of systemic limits and multiple crises – climate emergency, economic crash and pandemic. Instead of rebuilding the old flawed political economy again, what the world needs now is a more robust, just, ethical and equitable social-ecological economy
Essays on FinTech
Get PDFFinTech typically describes the application of novel technologies in the financial services sector. These technological innovations aim to compete with traditional financial technologies and improve user experience on a broad range of financial applications. Examples range from peer-to-peer investing services and new settlement procedures to the use of smartphones for mobile banking. Each chapter of this dissertation deals with one of these examples with the goal to draw conclusions for broader economic questions.
In the first chapter, Crowdfunding and Demand Uncertainty, I analyze the potential of reward-based crowdfunding to elicit demand information and improve the screening of viable projects vis-à-vis traditional external financing. Crowdfunding allows entrepreneurs to sell claims on future products directly to consumers to finance their investments. At the same time, this peer-to-peer sale of claims generates demand information that benefits the screening process for viable projects. I provide a characterization of the profit-maximizing crowdfunding mechanism when an entrepreneur knows neither the number of consumers who positively value the product nor their reservation prices. Using mechanism design theory, I show that the entrepreneur can finance all viable projects by committing to prices that decrease as the number of pledgers increases. This pricing strategy grants ex-post information rents to consumers with high reservation prices. However, if these information rents are large, then the entrepreneur prefers fixed high prices that lead to underinvestment since consumers with low valuations never participate.
The second chapter, Building Trust Takes Time: Limits to Arbitrage in Blockchain-Based Markets, is a joint project with Nikolaus Hautsch and Stefan Voigt. We analyze the potential implications of distributed ledger technologies, such as blockchain, for cross-market trading. Distributed ledgers replace trusted clearing counterparties and security depositories with time-consuming consensus protocols to record the transfer of ownership. We argue that this settlement latency exposes cross-market arbitrageurs to price risk and theoretically derive arbitrage bounds that increase with expected latency, latency uncertainty, volatility in the underlying asset, and arbitrageurs' risk aversion. We then use Bitcoin order book and network data to estimate arbitrage bounds of, on average, 121 basis points, which in fact explain 91% of the observed cross-market price differences in our sample period. Consistent with our theoretical framework, we also find that periods of high latency-implied price risk exhibit large price differences, while asset flows across exchanges chase arbitrage opportunities. Our main conclusion is that blockchain-based settlement introduces a non-trivial friction that impedes arbitrageurs' activity.
The third chapter, Perceived Precautionary Savings Motives: Evidence from FinTech, is coauthored with Francesco D'Acunto, Thomas Rauter, and Michael Weber. We use data from a European FinTech banking app provider to study the consumption response to the introduction of a mobile overdraft facility. In addition, we use the banking app to elicit consumers' preferences, beliefs, and motives. We find that users increase their spending permanently, lower their savings rate, and reallocate spending from non-discretionary to discretionary goods. Interestingly, users with a lot of deposits relative to their income react more than others but do not tap into negative deposits. We demonstrate that these results are not fully consistent with conventional models of financial constraints, buffer stock models, or present-bias preferences. We hence label this channel perceived precautionary savings motives: users with a lot of liquidity behave as if they had strong precautionary savings motives even though no observables, including the elicited preferences and beliefs, suggest they should
“Kultur-Token” Sustainable Business Model: Visualizing, Tokenizing, and Rewarding Mobility Behavior in Vienna, Austria
Get PDFThe report ”Kultur-Token Sustainable Business Model:Visualizing, Tokenizing, and
Rewarding Mobility Behavior in Vienna, Austria” is the result of an ongoing scientific
collaboration between the Research Institute for Cryptoeconomics and the City of Vienna.
This case study uses business modeling to understand the project Kultur-Token and serves
as a strategic tool for both the management team as well as external stakeholders. The
report documents the process of development of the Kultur-Token, describes it’s purpose
and features, the goals and stakeholders involved until the suspension of the test phase at
the end of march 2020, as the Covid-19 pandemic restricted both mobility and cultural
activities.Series: Working Paper Series / Institute for Cryptoeconomics / Interdisciplinary Researc