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Worst case search over a set of forecasting scenarios applied to financial stress-testing

  • 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.

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Metadaten
Verfasserangaben:Steffen Finck
DOI:https://doi.org/10.1145/3319619.3326835
ISBN:978-1-4503-6748-6
Titel des übergeordneten Werkes (Englisch):Proceedings of the Genetic and Evolutionary Computation Conference Companion
Verlag:ACM
Verlagsort:New York, NY, USA
Herausgeber:Manuel Lopez-Ibanez, Anne Auger, Thomas Stützle
Dokumentart:Konferenzveröffentlichung
Sprache:Englisch
Erscheinungsjahr:2019
Datum der Freischaltung:10.12.2019
Freies Schlagwort / Tag:constrained optimization; high-dimensional search space; risk management; simulation-based optimization; stress-testing
Seitenanzahl:9
Erste Seite:1722
Letzte Seite:1730
Organisationseinheit:Forschung / Forschungszentrum Business Informatics
DDC-Sachgruppen:500 Naturwissenschaften und Mathematik / 510 Mathematik
000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
JEL-Klassifikation:C Mathematical and Quantitative Methods / C5 Econometric Modeling / C53 Forecasting and Other Model Applications
C Mathematical and Quantitative Methods / C6 Mathematical Methods and Programming / C61 Optimization Techniques; Programming Models; Dynamic Analysis
Peer Review:wiss. Beitrag, peer-reviewed
Publikationslisten:Bibliographie 2019
Lizenz (Deutsch):License LogoUrhG - The Austrian Copyright Act applies - Es gilt das österr. Urheberrechtsgesetz