TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Finck, Steffen
ED  - Lopez-Ibanez, Manuel
ED  - Auger, Anne
ED  - Stützle, Thomas
T1  - Worst case search over a set of forecasting scenarios applied to financial stress-testing
T2  - Proceedings of the Genetic and Evolutionary Computation Conference Companion
N2  - 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.
KW  - risk management
KW  - simulation-based optimization
KW  - high-dimensional search space
KW  - constrained optimization
KW  - stress-testing
Y1  - 2019
SN  - 978-1-4503-6748-6
SB  - 978-1-4503-6748-6
U6  - https://doi.org/10.1145/3319619.3326835
DO  - https://doi.org/10.1145/3319619.3326835
SP  - 1722
EP  - 1730
S1  - 9
PB  - ACM
CY  - New York, NY, USA
ER  -