@inproceedings{Finck2019,
  author    = {Steffen Finck},
  title     = {Worst case search over a set of forecasting scenarios applied to financial stress-testing},
  series = {Proceedings of the Genetic and Evolutionary Computation Conference Companion},
  editor    = {Manuel Lopez-Ibanez and Anne Auger and Thomas St{\"u}tzle},
  publisher = {ACM},
  address   = {New York, NY, USA},
  isbn      = {978-1-4503-6748-6},
  doi       = {10.1145/3319619.3326835},
  pages     = {1722 -- 1730},
  year      = {2019},
  abstract  = {Stress testing is part of today’s bank risk management and often required by the governing regulatory authority. Performing such a stress test with stress scenarios derived from a distribution, instead of pre-defined expert scenarios, results in a systematic approach in which new severe scenarios can be discovered. The required scenario distribution is obtained from historical time series via a Vector-Autoregressive time series model. The worst-case search, i.e. finding the scenario yielding the most severe situation for the bank, can be stated as an optimization problem. The problem itself is a constrained optimization problem in a high-dimensional search space. The constraints are the box constraints on the scenario variables and the plausibility of a scenario. The latter is expressed by an elliptic constraint. As the evaluation of the stress scenarios is performed with a simulation tool, the optimization problem can be seen as black-box optimization problem. Evolution Strategy, a well-known optimizer for black-box problems, is applied here. The necessary adaptations to the algorithm are explained and a set of different algorithm design choices are investigated. It is shown that a simple box constraint handling method, i.e. setting variables which violate a box constraint to the respective boundary of the feasible domain, in combination with a repair of implausible scenarios provides good results.},
  language  = {en}
}