The framework promotes internal consistency, reduces subjectivity, and enhances public accountability. Key principles guiding haircut calibration include non-procyclicality, data-driven methodology, conservatism, and the elimination of arbitrage opportunities.

Beyond setting haircuts, the study highlights the importance of monitoring residual uncollateralised exposures, which can pose hidden risks during stress periods.

Researchers demonstrate how such exposures can be quantified using traditional financial models alongside advanced techniques, including Machine Learning. Variational Autoencoders are applied to simulate stress scenarios for bond yields and validate haircut models, showing potential to improve risk assessment.

The paper offers a robust framework for central banks to support financial stability and effective liquidity provision. Future research aims to extend the approach using artificial intelligence, including a “Super Economist” model that leverages a Large Language Model to convert Article IV reports into forward-looking quantitative indicators, enhancing sovereign bond spread construction for countries with limited data.

–IMF/ChannelAfrica–