Policy

My policy work informs decision-makers at the national, European, and global levels, thereby shaping financial stability policies. By nature, most of my policy contributions are confidential. Below is a selection of publicly available policy publications and public presentations.

Selected Policy Publications

A Price-at-Risk approach for the German commercial real estate market (with Jannick Plaasch and Florian Stammwitz)

Bundesbank Technical Paper 08/2024 (free PDF)

We apply the growth-at-risk model of Adrian et al. (2019) to the German commercial real estate (CRE) market. We derive a distribution for CRE price growth four quarters ahead conditional on macro-financial variables. This approach allows us to make probability statements about the downside risk to future CRE price growth, which serve as an input to financial stability analyses. We find that the conditional distribution has shifted strongly to the left since the COVID-19 pandemic, in line with deteriorating macroeconomic conditions, an increase in long-term interest rates and a decline in the net initial yield, resulting in lower expected price growth rates across the entire distribution.

Paper featured in the 2024 Bundesbank Financial Stability Review | Results featured in tagesschau.de, Reuters and FAZ

A Top-Down Loan-Level Stress Test for Banks’ Corporate Credit Risk: Application to Risks from Commercial Real Estate Markets (with Christoph Roling)

Bundesbank Technical Paper 09/2024 (free PDF)

We study the credit risk of banks in Germany from lending to non-financial firms. We model changes in Expected Credit Loss, which is derived from the guidelines in the IFRS 9 accounting standard. We map the accounting model to a dataset with individual loans as the unit of observation (AnaCredit). We present new approaches to modeling two well-known credit risk parameters: Loss Given Default (LGD), and Probability of Default (PD), which both affect Expected Credit Loss. First, we obtain an approximation of the Loss Given Default for each individual loan. This step makes use of the detailed collateral data available in AnaCredit and reveals a heterogeneity in LGD that is typically ignored in top-down stress tests. Second, regarding PD, we encounter a missing data problem since only a subset of banks reports default probabilities in AnaCredit. We employ machine learning algorithms to impute missing default probabilities. With the help of these credit risk parameters, we then apply the stress test model to two ad–hoc scenarios in which the downturn in CRE markets worsens to varying degrees and report how this would affect the capital of German banks.

Results featured in the 2024 Bundesbank Financial Stability Review, Bloomberg and Handelsblatt

Selected Public Presentations