Non-Parametric Robust Model Risk Measurement with Path-Dependent Loss Functions

Understanding and measuring model risk is important to financial practitioners. However, there lacks a non-parametric approach to model risk quantification in a dynamic setting and with path-dependent losses. We propose a complete theory generalizing the relative-entropic approach by Glasserman and Xu to the dynamic case under any f-divergence. It provides an unified treatment for measuring both the worst-case risk and the f-divergence budget that originate from the model uncertainty of an underlying state process