Peter Grundke Book order

Reverse stress tests are a relatively new stress test instrument that aims at finding exactly those scenarios that cause a bank to cross the frontier between survival and default. Afterward, the scenario which is most probable has to be identified. This paper sketches a framework for a quantitative reverse stress test for maturity-transforming banks that are exposed to credit and interest rate risk and demonstrates how the model can be calibrated empirically. The main features of the proposed framework are: 1) The necessary steps of a reverse stress test (solving an inversion problem and computing the scenario probabilities) can be performed within one model, 2) Scenarios are characterized by realizations of macroeconomic risk factors, 3) Principal component analysis helps to reduce the dimensionality of the space of systematic risk factors, 4) Due to data limitations, the results of reverse stress tests are exposed to considerable model and estimation risk, which makes numerous robustness checks necessary.

Banks are exposed to various kinds of risks; among them are credit default risks, market price risks and operational risks the most important ones. Aggregating these different risk ex- sures to a comprehensive risk position is an important, yet challenging and up to now un- solved task. Banks’ current state of the art in risk management is still far away from achieving a fully integrated view of the risks they are exposed to. This shortfall traces back to both, to conceptual problems of constructing an appropriate risk model and to the computational b- den of calculating a loss distribution. The approach presented in this book takes credit default risk as a starting point. By integrating market risks, a general credit risk model is constructed that comprises the standard industry credit risk models as special cases. Within the framework of this general credit risk model, the effects of simplifying assumptions that are typical for standard credit risk models can be a- lyzed. Important insights gained by this analysis are that neglecting market price risks and losses given default correlated to default rates can cause a significant understatement of value at risk figures.