JOURNAL ARTICLE
The Joint Determination of Haircuts and Interest Rates for Collateralized Loans in Shadow Banking.
Published In: Management Science (INFORMS), 2025, v. 71, n. 11. P. 9485 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Hao, Jinji 3 of 3
Abstract
This article develops a general equilibrium model to analyze the joint determination of haircuts and interest rates on collateralized loans within the shadow banking system. It finds that improvements in collateral quality affect haircuts asymmetrically: higher upside quality always increases haircuts, while higher downside quality reduces haircuts only under certain conditions involving the levels of upside and downside quality. The model explains the empirical observation that interest rates are relatively insensitive to shocks compared to haircuts, attributing this to the nature of shocks on upside collateral quality and endogenous adjustments in bond face values and prices. Additionally, it predicts that increases in depositors' saving needs or reductions in banks' collateral supply lead to higher interest rates but lower haircuts, a pattern contrary to conventional banking intuition. These findings have implications for understanding repo markets, particularly those backed by riskier nongovernment collateral, and suggest testable empirical specifications for the relationship between collateral quality, haircuts, and interest rates.
Additional Information
- Source:Management Science (INFORMS). 2025/11, Vol. 71, Issue 11, p9485
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2025
- ISSN:0025-1909
- DOI:10.1287/mnsc.2022.00541
- Accession Number:189064385
- Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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