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Cascade Sensitivity Measures.

Silvana M Pesenti1, Pietro Millossovich2,3, Andreas Tsanakas3

  • 1Department of Statistical Sciences, University of Toronto, 700 University Avenue, Toronto, Ontario, M5G 1X6, Canada.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|January 28, 2022
PubMed
Summary
This summary is machine-generated.

Cascade sensitivity measures how changes in one input factor affect model outputs, considering indirect impacts from dependent factors. This novel approach enhances risk analysis for complex systems.

Keywords:
Rosenblatt transformSensitivity analysisdependenceimportance measuresmodel uncertaintyrisk measures

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Area of Science:

  • Quantitative risk analysis
  • Mathematical modeling in finance

Background:

  • Sensitivity measures in risk analysis assess output distribution changes due to input factor variations.
  • Existing methods may not fully capture indirect effects in statistically dependent input factors.

Purpose of the Study:

  • To introduce a novel sensitivity measure, 'cascade sensitivity', for risk analysis.
  • To account for both direct and indirect impacts of stressed input factors in dependent systems.

Main Methods:

  • Defining cascade sensitivity as a derivative of a risk measure with respect to a transformed input vector.
  • Deriving alternative representations to handle model incompleteness and computational costs.
  • Applying the methodology to a commercial insurance risk model.

Main Results:

  • The cascade sensitivity measure explicitly captures direct and indirect effects of input factor stresses.
  • Alternative representations offer practical solutions for real-world risk analysis challenges.
  • The method's applicability is demonstrated in an insurance risk context.

Conclusions:

  • Cascade sensitivity provides a more comprehensive understanding of risk propagation in models with dependent inputs.
  • The derived methods enhance the practical implementation of sensitivity analysis in complex risk assessments.
  • This approach improves the robustness of risk analysis for financial and insurance models.