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A severity function approach to scenario selection

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Bibliographic Details
Authors and Corporations: Mokinski, Frieder (Author)
Type of Resource: E-Book
Language: English
published:
Frankfurt am Main Deutsche Bundesbank [04.12.2017]
Series: Deutsche Bundesbank: Discussion paper ; no 2017, 34
Subjects:
Stress Testing
Conditional Forecasting
Density Forecasting
Time series
Bayesian VAR
Simulation
Arbeitspapier
Graue Literatur
Source: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
ISBN: 9783957294098
Description
Summary: The severity function approach (abbreviated SFA) is a method of selecting adverse scenarios from a multivariate density. It requires the scenario user (e.g. an agency that runs banking sector stress tests) to specify a "severity function", which maps candidate scenarios into a scalar severity metric. The higher the value of this metric, the more harmful a scenario is. In selecting a scenario the SFA proceeds as follows: First, it isolates a set of equally severe scenario candidates. This set is determined by the condition that more severe scenarios only occur with some user-specified probability. Second, from this set it selects the candidate with the highest probability density, i.e. the most plausible scenario. The approach hence operationalizes the mantra that "scenarios should be severe yet plausible".
Physical Description: 1 Online-Ressource (circa 28 Seiten); Illustrationen
ISBN: 9783957294098