A severity function approach to scenario selection

Frieder Mokinski

No 34/2017, Discussion Papers from Deutsche Bundesbank

Abstract: 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".

Keywords: Stress Testing; Conditional Forecasting; Density Forecasting; Time series; Bayesian VAR; Simulation (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 C61 G01 G32 (search for similar items in EconPapers)
Date: 2017
New Economics Papers: this item is included in nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:bubdps:342017

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