{
  "title": "A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management",
  "publication": "IMF Working Papers, October 31, 2025",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2025/10/31/a-quantitative-approach-to-central-bank-haircuts-and-counterparty-risk-management-571472",
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  "summary": "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.",
  "sections": [
    {
      "heading": "Framework for haircut setting",
      "content": "- Introduces four haircut model types tailored to asset characteristics—marketable or non-marketable—and data availability.\n- Proposes a novel model for setting haircuts in data-limited environment using a satallite cross-country model.\n- Key principles guiding haircut calibration:\n  - non-procyclicality\n  - data-drivenness\n  - conservatism\n  - avoidance of arbitrage gaps\n- Model inputs detailed:\n  - Value-at-Risk (VaR) percentiles\n  - volatility measures\n  - time to liquidation"
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    {
      "heading": "Quantifying residual uncollateralized exposures",
      "content": "- Proposes a quantitative framework for estimating expected uncollateralized exposures that remain after haircut application.\n- Emphasizes the importance of expected uncollateralized exposures in stress scenarios.\n- Illustrative simulations demonstrate how volatility impacts exposure using dynamic Nelson-Siegel yield curve models."
    },
    {
      "heading": "Machine learning for validation and stress simulation",
      "content": "- Explores the use of Variational Autoencoders (VAEs) to simulate stress scenarios for bond yields.\n- VAEs trained on U.S. Treasury data capture realistic yield curve distributions and offer an alternate tool for validating VaR-based haircuts.\n- Notes and caveats:\n  - Interpretability and explainability remain concerns for machine learning models.\n  - Machine learning models can enhance risk assessment by uncovering potential model vulnerabilities.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2025/10/31/a-quantitative-approach-to-central-bank-haircuts-and-counterparty-risk-management-571472"
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    "Authors: Yuji Sakurai",
    "Published: October 31, 2025",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798229026819.001",
    "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:",
    "Model inputs detailed:",
    "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.",
    "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:",
    "**Working Paper**"
  ],
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