Difference between revisions of "Sensitivity Analysis"
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== Definition == | == Definition == | ||
| − | '''Sensitivity Analysis''' | + | '''Sensitivity Analysis''' is a method to understand differences resulting from methodological choices and assumptions and to explore model sensitivities to inputs. The method involves varying the parameters to understand the sensitivity of the overall results to changes in those parameters. |
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| + | Sensitivity analyis is a quantitative technique which (e.g. in a [[Model Validation]] context) can establish the '''robustness''' of a given [[Risk Model]]. In practise, if small variations to inputs lead to large variations in outcomes the sensitivity analysis suggests that the model / framework requires additional attention. | ||
== Context == | == Context == | ||
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== Issues and Challenges == | == Issues and Challenges == | ||
| − | * Sensitivity analysis will not identify vulnerabilities linked to a [[Risk Factor]] and phenomena that has been ignored in the formulation of the [[Model Assumptions]] | + | * Sensitivity analysis will not identify vulnerabilities linked to a missing [[Risk Factor]] and phenomena that has been ignored in the formulation of the [[Model Assumptions]] |
== References == | == References == | ||
Latest revision as of 14:18, 27 October 2021
Definition
Sensitivity Analysis is a method to understand differences resulting from methodological choices and assumptions and to explore model sensitivities to inputs. The method involves varying the parameters to understand the sensitivity of the overall results to changes in those parameters.
Sensitivity analyis is a quantitative technique which (e.g. in a Model Validation context) can establish the robustness of a given Risk Model. In practise, if small variations to inputs lead to large variations in outcomes the sensitivity analysis suggests that the model / framework requires additional attention.
Context
We can broadly distinguish inputs as those that normally remain constant during model operation (Model Parameters) and these inputs that vary (market observables, client data etc.). Model sensitivity can be defined with respect to both classes. In the case of variable inputs the sensitivity indicates the range of variation that can be expected in the normal operation of the model. In the case of non-varying model parameters the sensitivity analysis can reveal the variation of outcomes hidden in possibly hard to fix parameters.
Issues and Challenges
- Sensitivity analysis will not identify vulnerabilities linked to a missing Risk Factor and phenomena that has been ignored in the formulation of the Model Assumptions
