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

## TL;DR

This paper develops a comprehensive non-parametric framework for dynamic, path-dependent model risk measurement using $f$-divergences, extending existing entropic methods to more general settings.

## Contribution

It generalizes the relative-entropic approach to dynamic, path-dependent losses under any $f$-divergence, providing a unified theory for model risk quantification.

## Key findings

- Unified treatment of worst-case risk and divergence budget
- Extension of entropic methods to path-dependent, dynamic settings
- Applicable to various $f$-divergences in model risk measurement

## Abstract

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.