A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management

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This paper presents a comprehensive framework for determining haircuts on collateral used in central bank operations, quantifying residual uncollateralized exposures, and validating haircut models using machine learning. First, it introduces four haircut model types tailored to asset characteristics-marketable or non-marketable-and data availability. It proposes a novel model for setting haircuts in data-limited environment using a satallite cross-country model. Key principles guiding haircut calibration include non-procyclicality, data-drivenness, conservatism, and the avoidance of arbitrage gaps. The paper details model inputs such as Value-at-Risk (VaR) percentiles, volatility measures, and time to liquidation. Second, it proposes a quantitative framework for estimating expected uncollateralized exposures that remain after haircut application, emphasizing their importance in stress scenarios. Illustrative simulations using dynamic Nelson-Siegel yield curve models demonstrate how volatility impacts exposure. Third, the paper explores the use of Variational Autoencoders (VAEs) to simulate stress scenarios for bond yields. Trained on U.S. Treasury data, VAEs capture realistic yield curve distributions, offering an altenative tool for validating VaR-based haircuts. Although interpretability and explainability remain concerns, machine learning models enhance risk assessment by uncovering potential model vulnerabilities.

Subject: Bonds, Collateral, Credit risk, Econometric analysis, Financial institutions, Financial regulation and supervision, Financial services, Vector autoregression, Yield curve

Keywords: Bonds, Collateral, Credit, Credit risk, Haircuts, Machine Learning, Uncollateralized Exposure, Vector autoregression, Yield curve