Bayesian backtesting for counterparty risk models

Mante Zelvyte and Matthias Arnsdorf

Journal of Risk Model Validation

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.

References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.risk.net/journal-of-risk-model-validat ... terparty-risk-models (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rsk:journ5:7956866

Access Statistics for this article

More articles in Journal of Risk Model Validation from Journal of Risk Model Validation
Bibliographic data for series maintained by Thomas Paine (maintainer@infopro-digital.com).