JOURNAL ARTICLE
Counterparty Risk and Counterparty Choice in the Credit Default Swap Market.
Published In: Management Science (INFORMS), 2024, v. 70, n. 6. P. 3808 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Du, Wenxin; Gadgil, Salil; Gordy, Michael B.; Vega, Clara 3 of 3
Abstract
This article investigates how market participants price and manage counterparty credit risk in the single-name credit default swap (CDS) market using confidential transaction-level data from the Depository Trust & Clearing Corporation (DTCC) covering 2010–2020. It finds that counterparty credit risk has a statistically significant but economically modest effect on CDS pricing, while strongly influencing the choice of counterparties, with clients avoiding dealers of lower credit quality and those exhibiting high wrong-way risk (i.e., whose credit risk is correlated with the reference entity). For contracts eligible for central clearing, the study shows that nondealer clients are more likely to clear trades during crises and when reference entities are illiquid, but less likely to clear when the reference entity is a large U.S. dealer or sovereign, reflecting residual systemic risk. Overall, central clearing reduces transaction spreads and alters counterparty selection dynamics, highlighting that counterparty risk management in CDS markets relies more on counterparty choice and clearing decisions than on price adjustments.
Additional Information
- Source:Management Science (INFORMS). 2024/06, Vol. 70, Issue 6, p3808
- Document Type:Article
- Subject Area:Business and Management
- Publication Date:2024
- ISSN:0025-1909
- DOI:10.1287/mnsc.2023.4870
- Accession Number:177878306
- 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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