  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.
