Bayesian backtesting for counterparty risk models

Mante Zelvyte and Matthias Arnsdorf

Abstract

We introduce a new framework for counterparty risk model backtesting based on Bayesian methods. This provides a conceptually sound approach for analyzing model performance that is also straightforward to implement. We show that our methodology provides important advantages over a typical, classical backtesting setup. In particular, we find that the Bayesian approach outperforms the classical one in identifying whether a model is correctly specified, which is the principal aim of any backtesting framework. The power of the methodology is due to its ability to test individual parameters and thus identify not only the degree of misspecification but also which aspects of a model are misspecified. This greatly facilitates the impact assessment of model issues as well as their remediation.