A severity function approach to scenario selection

Arbeitspapier

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".

Object Details

• ISBN: 978-3-95729-409-8
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Bundesbank Discussion Paper ; No. 34/2017

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Multiple or Simultaneous Equation Models: Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models ; Forecasting Models; Simulation Methods ; Optimization Techniques; Programming Models; Dynamic Analysis ; Financial Crises ; Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
• Subject: Stress Testing ; Conditional Forecasting ; Density Forecasting ; Time series ; Bayesian VAR ; Simulation

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Mokinski, Frieder
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2017

Further information

• Handle: http://hdl.handle.net/10419/172002
• Last update: 10.03.2025, 11:44 AM CET