JOURNAL ARTICLE

Intermediary Capital and the Credit Market.

  • Published In: Management Science (INFORMS), 2025, v. 71, n. 1. P. 162 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Harris, Milton; Opp, Christian C.; Opp, Marcus M. 3 of 3

Abstract

This article develops a tractable equilibrium framework to analyze the role of regulated financial intermediaries' capital in the allocation and pricing of credit across heterogeneous borrowers. It introduces a sufficient statistic—the total return on intermediary capital—that governs intermediaries' lending decisions and derives a novel intermediary asset pricing equation accounting for endogenous segmentation of marginal investors across securities. The model highlights how intermediaries' shadow cost of capital and implicit government subsidies (the "put value") influence credit composition, pricing distortions, and risk-taking incentives, explaining empirical phenomena such as reaching-for-yield behavior, rating effects on loan yields, concentrated portfolios, and substitution between bank and public market financing. Additionally, the framework provides insights into the compositional effects of regulatory policies like capital ratio requirements and the impact of shocks to intermediary capital, emphasizing the interplay between borrower heterogeneity, regulatory constraints, and market structure in shaping credit markets.

Additional Information

  • Source:Management Science (INFORMS). 2025/01, Vol. 71, Issue 1, p162
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2025
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2020.01536
  • Accession Number:182281725
  • 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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