The Credibility Theory applied to backtesting Counterparty Credit Risk

TL;DR

This paper applies credibility theory to improve backtesting of interest rate forecasts in counterparty credit risk, demonstrating enhanced reliability of test results with limited data for regulatory validation.

Contribution

It introduces a novel application of credibility theory to backtesting in counterparty credit risk, aiding risk managers in assessing model reliability with small samples.

Findings

Credibility-adjusted confidence intervals increase test reliability.

Application aligns with regulatory validation requirements.

Enhanced model assessment with limited data.

Abstract

Credibility theory provides tools to obtain better estimates by combining individual data with sample information. We apply the Credibility theory to a Uniform distribution that is used in testing the reliability of forecasting an interest rate for long term horizons. Such empirical exercise is asked by Regulators (CRR, 2013) in validating an Internal Model Method for Counterparty Credit Risk. The main results is that risk managers consider more reliable the output of a test with limited sample size when the Credibility is applied to define a confidence interval.