Bayesian backtesting for counterparty risk models

28 Pages Posted: 6 Jun 2023

See all articles by Mante Zelvyte

Mante Zelvyte

JP Morgan Chase & Co.; University College London

Matthias Arnsdorf

JP Morgan

Multiple version iconThere are 2 versions of this paper

Date Written: February 28, 2023


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.

Keywords: counterparty risk, exposure models, backtesting, Bayesian methods, model risk

Suggested Citation

Zelvyte, Mante and Arnsdorf, Matthias, Bayesian backtesting for counterparty risk models (February 28, 2023). Journal of Risk Model Validation, Vol. 17, No. 2, 2023, Available at SSRN:

Mante Zelvyte

JP Morgan Chase & Co. ( email )

25 Bank Street
London, E14 5JP
United Kingdom

HOME PAGE: http://

University College London ( email )

1-19 Torrington Place
London, WC1 7HB
United Kingdom

HOME PAGE: http://

Matthias Arnsdorf (Contact Author)

JP Morgan ( email )

United Kingdom

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