Research

Der Forschungsbereich Real Estate Finance besch?ftigt sich mit finanzwirtschaftlichen Fragestellungen rund um Immobilienm?rkte und immobilienbezogene Kapitalanlagen. Im Fokus stehen die Analyse von Rendite-Risiko-Profilen, die Bewertung von Immobilieninvestitionen sowie die Interaktion zwischen Immobilien und Finanzm?rkten.

Ein besonderer Schwerpunkt liegt auf der Untersuchung b?rsennotierter Immobilienunternehmen (REITs), deren Preisbildung und Risikostrukturen. Dabei kommen moderne empirische Methoden zum Einsatz, um die zeitliche Dynamik von Marktverhalten und Einflussfaktoren zu erfassen. Neben klassischen ?konometrischen Verfahren werden zunehmend auch Ans?tze aus dem Bereich des maschinellen Lernens verwendet, um komplexe Zusammenh?nge transparent und erkl?rbar zu machen.

Darüber hinaus widmet sich die Forschung aktuellen Entwicklungen wie der Digitalisierung von Verm?genswerten und der Entstehung virtueller Immobilienm?rkte. Auch die Wechselwirkungen zwischen Immobilienanlagen und alternativen Anlageklassen – etwa Kryptow?hrungen – werden analysiert, um neue Formen der Kapitalallokation besser zu verstehen.

Ziel ist es, ein tieferes Verst?ndnis für die Funktionsweise und die Rolle von Immobilien im Finanzsystem zu schaffen und fundierte Entscheidungsgrundlagen für Wissenschaft, Praxis und Regulierung bereitzustellen.

Risk management is a key challenge for all financial institutions and involves identifying, assessing, and managing financial risks. In a dynamic, globally interconnected economy, companies, banks, and investors face daily uncertainties that can jeopardize their financial stability—such as interest rate changes, currency fluctuations, market volatility, credit risks, value fluctuations in crypto assets, or operational risks.
Our research in the field of risk management aims to systematically understand these risks and develop effective strategies to minimize potential losses. We use quantitative methods, mathematical models, and modern technologies such as artificial intelligence and machine learning. The goal is not only to analyze past developments, but also to identify future risks at an early stage in order to enable informed decisions and develop effective risk strategies. A particular focus is on the resilience of financial institutions to crisis scenarios and external shocks, such as macroeconomic shocks.
As part of our research, we contribute to the development of new concepts for integrated risk management that takes a holistic view of market, credit, liquidity, and operational risks. Regulatory aspects—such as compliance with international standards (e.g., Basel III/IV, Solvency II)—are also the subject of our research.

Credit risk analytics is a key area of research in modern finance that deals with the quantitative analysis and management of credit risks. In an increasingly complex, globally interconnected financial world, it is crucial to accurately measure, forecast, and manage credit default risks. Our research in this area aims to strengthen the stability of both individual financial institutions and the financial system as a whole.
Essentially, we address the following questions: How likely is it that a borrower will default on their payment obligations, and how high could the resulting losses be? Determining this requires a deep understanding of the economic environment, the creditworthiness of debtors, and market behavior. In our research, we use state-of-the-art statistical methods, machine learning, and simulation techniques to analyze historical data and better predict future developments.
A particular focus is on the development of innovative models for estimating probabilities of default, loss given default, and exposure at default. These variables form the basis of regulatory capital requirements under international standards such as Basel III and IV and are therefore also important for central banks and supervisory authorities. We are developing new approaches to risk measurement that go beyond traditional credit ratings. The use of artificial intelligence and big data makes it possible to include unstructured data sources such as text documents, news, and social media in the analysis. This allows credit risks to be identified more quickly and accurately, even in volatile or less transparent markets.
At the same time, we are working on determining systemic risks. The key question here is how the defaults of individual market participants are interdependent. In particular, we are developing innovative approaches for measuring borrower dependencies and correlations. Our international collaborations with banks, management consultancies, supervisory authorities, and universities are therefore essential for developing solutions that are both practical and scientifically sound.
Our research area of credit risk analytics thus contributes to the stability and sustainability of the global financial system. By further developing analytical methods, we make a decisive contribution to the early detection of financial risks, better management of credit portfolios, and the resilience of financial markets worldwide.