A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management
IMF Working Papers, October 31, 2025
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- A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management
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Bibliographic details
- Authors: Yuji Sakurai
- Published: October 31, 2025
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798229026819.001
Framework for haircut setting
- Introduces four haircut model types tailored to asset characteristics—marketable or non-marketable—and data availability.
- Proposes a novel model for setting haircuts in data-limited environment using a satallite cross-country model.
- Key principles guiding haircut calibration:
- non-procyclicality
- data-drivenness
- conservatism
- avoidance of arbitrage gaps
- Model inputs detailed:
- Value-at-Risk (VaR) percentiles
- volatility measures
- time to liquidation
Quantifying residual uncollateralized exposures
- Proposes a quantitative framework for estimating expected uncollateralized exposures that remain after haircut application.
- Emphasizes the importance of expected uncollateralized exposures in stress scenarios.
- Illustrative simulations demonstrate how volatility impacts exposure using dynamic Nelson-Siegel yield curve models.
Machine learning for validation and stress simulation
- Explores the use of Variational Autoencoders (VAEs) to simulate stress scenarios for bond yields.
- VAEs trained on U.S. Treasury data capture realistic yield curve distributions and offer an alternate tool for validating VaR-based haircuts.
- Notes and caveats:
- Interpretability and explainability remain concerns for machine learning models.
- Machine learning models can enhance risk assessment by uncovering potential model vulnerabilities.
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- Working Paper