Quantification of model risk in stress testing and scenario analysis

Abstract

Understanding and quantifying the model risk inherent in loss projection models used in the macroeconomic stress testing and impairment estimation is of significant concern for both banks and regulators. The application of relative entropy techniques allow model misspecification robustness to be numerically quantified using exponential tilting towards an alternative probability law. Using a particular loss forecasting model we quantify the model worst-case loss term-structures to yield insight into the behavior of the worst-case. The worst-case obtained represents in general an upward scaling of the term-structure consistent with the exponential tilting adjustment. The relative entropy approach to model risk we use has its foundation in economics with robust forecasting analysis and has recently started to be applied in risk management. The technique can complement the traditional model risk quantification techniques where a specific direction or range of model misspecification reasons are usually considered, such as, model sensitivity analysis, model parameter uncertainty analysis, competing models, and, conservative model assumptions.