Refine
Year of publication
Document Type
- Article (9)
- Conference Proceeding (4)
- Report (4)
- Book (3)
- Part of a Book (2)
Institute
Is part of the Bibliography
- yes (22)
Keywords
- convex integral functional (4)
- Bregman distance (3)
- Model risk (3)
- Multiple priors (3)
- Relative entropy (3)
- Risk measures (3)
- Scenario analysis (3)
- Stress testing (3)
- f-divergence (3)
- Convex functions (2)
- I-divergence (2)
- Mathematical model (2)
- Systemic risk (2)
- Worst case (2)
- f-divergence ball (2)
- finance (2)
- generalized exponential family (2)
- information geometry (2)
- maximum entropy principle (2)
- Agent based modeling (1)
- Algorithm Analysis and Problem Complexity (1)
- Almost worst case densities (1)
- Artificial Intelligence (incl. Robotics) (1)
- Asset pricing (1)
- Bregman ball (1)
- Bregman distance ball (1)
- Computer Graphics (1)
- Covariance matrix adaptation (1)
- Credit risk (1)
- Discrete Mathematics in Computer Science (1)
- Double auction (1)
- Educational institutions (1)
- Entropy (1)
- Evolution strategy (1)
- Fire sales (1)
- I-divergence ball (1)
- Image Processing and Computer Vision (1)
- Information theory (1)
- Levenberg-Marquardt algorithm (1)
- Leverage (1)
- Maximum loss (1)
- Minimization (1)
- Pattern Recognition (1)
- Payoff function (1)
- Pythagorean identity (1)
- Random variables (1)
- Risk measure (1)
- Trade on networks (1)
- Uncertainty (1)
- Worst case density (1)
- Worst case search (1)
- almost worst scenarios (1)
- ambiguity aversion (1)
- convex integral functionals (1)
- divergence preferences (1)
- expected value minimization (1)
- geometric intuition (1)
- geometry (1)
- information theoretic multiple priors (1)
- level set (1)
- mathematical finance (1)
- minimisation (1)
- moment constraints (1)
- plausible prior distributions (1)
- portfolio optimization (1)
- probability (1)
- risk analysis (1)
- risk measure (1)
- self-adaptation (1)
For a given set of banks, how big can losses in bad economic or financial scenarios possibly get, and what are these bad scenarios? These are the two central questions of stress tests for banks and the banking system. Current stress tests select stress scenarios in a way which might leave aside many dangerous scenarios and thus create an illusion of safety; and which might consider highly implausible scenarios and thus trigger a false alarm. We show how to select scenarios systematically for a banking system in a context of multiple credit exposures. We demonstrate the application of our method in an example on the Spanish and Italian residential real estate exposures of European banks. Compared to the EBA 2016 stress test our method produces scenarios which are equally plausible as the EBA stress scenario but yield considerably worse system wide losses.