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Counterparty Risk in Over-the-Counter Markets

Published online by Cambridge University Press:  09 August 2021

Christoph Frei Open the ORCID record for Christoph Frei [Opens in a new window] ,
Agostino Capponi  and
Celso Brunetti
Christoph Frei*
Affiliation:
University of Alberta Department of Mathematical and Statistical Sciences
Agostino Capponi
Affiliation:
Columbia University Department of Industrial Engineering and Operations Researchac3827@columbia.edu
Celso Brunetti
Affiliation:
Board of Governors of the Federal Reserve System celso.brunetti@frb.gov
*
cfrei@ualberta.ca (corresponding author)
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Abstract

We study trading and risk management decisions of banks in over-the-counter markets, accounting for 2 types of risk: payoff risk from loans and counterparty risk from trading activities. Our model provides empirically supported predictions on the structure of the interbank credit default swap (CDS) market: i) banks with high default probabilities either buy or sell CDS contracts; ii) because of the counterparty risk friction, payoff risk is only partially shared; and iii) safe banks act as intermediaries and help diversify counterparty risk. Banks manage their default probabilities to become creditworthy counterparties, but they do so in a socially inefficient way.

Type
Research Article
Information
Journal of Financial and Quantitative Analysis , Volume 57 , Issue 3 , May 2022 , pp. 1058 - 1082
Copyright
© The Author(s), 2021. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington

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Footnotes

We are grateful for constructive comments by Jack Bao, Garth Baughman, Jennifer Conrad (the editor), Co-Pierre George (discussant), Michael Gordy, Christian Heyerdahl-Larsen (discussant), Julien Hugonnier, Ricardo Lagos, Semyon Malamud, Artem Neklyudov, Semih Üslü (the referee), Pierre-Olivier Weill, and seminar participants at the Board of Governors of the Federal Reserve System, the Swiss Finance Institute at EPFL, the Economic Department at Indiana University, the 2017 Conference of the Northern Finance Association, and the 2018 Annual Meeting of the European Finance Association. We thank Alice Moore and Nathan Heinrich for their excellent assistance in the analysis of data. Frei thanks the Natural Sciences and Engineering Council of Canada for support under grant RGPIN/2019–04789. The research of Capponi is supported by an NSF-CMMI: 1752326 CAREER grant.

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