IMF-央行减持与交易对手风险管理的定量方法（英）

A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management Yuji Sakurai WP/25/225 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. 2025 OCT * I would like to deeply thank Romain Veyrune for encouraging me to conduct this research. We are grateful to Meguy KueteNgougning, Emre Balibek, Tetsuo Kurosaki, Toshinao Yoshiba, Goshima Keiichi, and the seminar participants at the IMF for their comments. All remaining errors are my own. © 2025 International Monetary Fund WP/25/225IMF Working Paper Monetary and Capital Markets Department A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management Prepared by Yuji Sakurai Authorized for distribution by Romain Veyrune October 2025 IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management. ABSTRACT: 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. RECOMMENDED CITATION: Sakurai, Y. “A Quantitative Approach to Central Bank Haircuts and Counterparty Risk Management” JEL Classification Numbers: G21, G28, G32, E58, C63 Keywords: Haircuts; Uncollateralized Exposure; Machine Learning Author’s E-Mail Address: ysakurai@imf.org Contents Intr