IDEAS home Printed from https://ideas.repec.org/a/taf/apmtfi/v27y2020i6p520-548.html
   My bibliography  Save this article

Counterparty Credit Limits: The Impact of a Risk-Mitigation Measure on Everyday Trading

Author

Listed:
  • Martin D. Gould
  • Nikolaus Hautsch
  • Sam D. Howison
  • Mason A. Porter

Abstract

A counterparty credit limit (CCL) is a limit that is imposed by a financial institution to cap its maximum possible exposure to a specified counterparty. CCLs help institutions to mitigate counterparty credit risk via selective diversification of their exposures. In this paper, we analyse how CCLs impact the prices that institutions pay for their trades during everyday trading. We study a high-quality data set from a large electronic trading platform in the foreign exchange spot market that allows institutions to apply CCLs. We find empirically that CCLs had little impact on the vast majority of trades in this data set. We also study the impact of CCLs using a new model of trading. By simulating our model with different underlying CCL networks, we highlight that CCLs can have a major impact in some situations.

Suggested Citation

  • Martin D. Gould & Nikolaus Hautsch & Sam D. Howison & Mason A. Porter, 2020. "Counterparty Credit Limits: The Impact of a Risk-Mitigation Measure on Everyday Trading," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(6), pages 520-548, November.
    • Handle: RePEc:taf:apmtfi:v:27:y:2020:i:6:p:520-548
    DOI: 10.1080/1350486X.2021.1893770
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/1350486X.2021.1893770
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/1350486X.2021.1893770?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Craig Pirrong, 2012. "Clearing and Collateral Mandates: A New Liquidity Trap?," Journal of Applied Corporate Finance, Morgan Stanley, vol. 24(1), pages 67-73, March.
    2. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
    3. Thorsten V. Koeppl, 2013. "The Limits Of Central Counterparty Clearing: Collusive Moral Hazard And Market Liquidity," Working Paper 1312, Economics Department, Queen's University.
    4. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    5. Craig, Ben & von Peter, Goetz, 2014. "Interbank tiering and money center banks," Journal of Financial Intermediation, Elsevier, vol. 23(3), pages 322-347.
    6. William Perraudin & Paolo Vitale, 1996. "Interdealer Trade and Information Flows in a Decentralized Foreign Exchange Market," NBER Chapters, in: The Microstructure of Foreign Exchange Markets, pages 73-106, National Bureau of Economic Research, Inc.
    7. Bruno Biais & Florian Heider & Marie Hoerova, 2012. "Clearing, Counterparty Risk, and Aggregate Risk," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(2), pages 193-222, July.
    8. Lee, Charles M C & Ready, Mark J, 1991. "Inferring Trade Direction from Intraday Data," Journal of Finance, American Finance Association, vol. 46(2), pages 733-746, June.
    9. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
    10. Rehlon, Amandeep & Nixon, Dan, 2013. "Central counterparties: what are they, why do they matter and how does the Bank supervise them?," Bank of England Quarterly Bulletin, Bank of England, vol. 53(2), pages 147-156.
    11. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2010. "Limit Order Books," Papers 1012.0349, arXiv.org, revised Apr 2013.
    12. Robert M. May & Simon A. Levin & George Sugihara, 2008. "Ecology for bankers," Nature, Nature, vol. 451(7181), pages 893-894, February.
    13. Robert A. Jarrow & Fan Yu, 2008. "Counterparty Risk and the Pricing of Defaultable Securities," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 20, pages 481-515, World Scientific Publishing Co. Pte. Ltd..
    14. Morten Bech, 2012. "FX volume during the financial crisis and now," BIS Quarterly Review, Bank for International Settlements, March.
    15. Fricke, Daniel & Lux, Thomas, 2012. "Core-periphery structure in the overnight money market: Evidence from the e-MID trading platform," Kiel Working Papers 1759, Kiel Institute for the World Economy (IfW Kiel).
    16. Roll, Richard, 1984. "A Simple Implicit Measure of the Effective Bid-Ask Spread in an Efficient Market," Journal of Finance, American Finance Association, vol. 39(4), pages 1127-1139, September.
    17. Fricke, Daniel, 2012. "Trading strategies in the overnight money market: Correlations and clustering on the e-MID trading platform," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(24), pages 6528-6542.
    18. Jeffrey A. Frankel & Giampaolo Galli & Alberto Giovannini, 1996. "The Microstructure of Foreign Exchange Markets," NBER Books, National Bureau of Economic Research, Inc, number fran96-1, March.
    19. Philippe Jorion & Gaiyan Zhang, 2009. "Credit Contagion from Counterparty Risk," Journal of Finance, American Finance Association, vol. 64(5), pages 2053-2087, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gould, Martin D. & Hautsch, Nikolaus & Howison, Sam D. & Porter, Mason A., 2017. "Counterparty credit limits: An effective tool for mitigating counterparty risk?," CFS Working Paper Series 581, Center for Financial Studies (CFS).
    2. Andre R. Neveu, 2018. "A survey of network-based analysis and systemic risk measurement," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(2), pages 241-281, July.
    3. Fabio Caccioli & Paolo Barucca & Teruyoshi Kobayashi, 2018. "Network models of financial systemic risk: a review," Journal of Computational Social Science, Springer, vol. 1(1), pages 81-114, January.
    4. Jin-Wook Chang, 2019. "Collateralized Debt Networks with Lender Default," Finance and Economics Discussion Series 2019-083, Board of Governors of the Federal Reserve System (U.S.).
    5. Qian Qian & Yang Yang & Zong-Fang Zhou, 2019. "Research on Trade Credit Spreading and Credit Risk within the Supply Chain," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(01), pages 389-411, January.
    6. Lux, Thomas, 2016. "Network effects and systemic risk in the banking sector," FinMaP-Working Papers 62, Collaborative EU Project FinMaP - Financial Distortions and Macroeconomic Performance: Expectations, Constraints and Interaction of Agents.
    7. León, C., 2015. "Financial stability from a network perspective," Other publications TiSEM bb2e4e44-e842-45c6-a946-4, Tilburg University, School of Economics and Management.
    8. Blasques, Francisco & Bräuning, Falk & Lelyveld, Iman van, 2018. "A dynamic network model of the unsecured interbank lending market," Journal of Economic Dynamics and Control, Elsevier, vol. 90(C), pages 310-342.
    9. Chen, Pei-wen & Huang, Han-ching & Su, Yong-chern, 2014. "The central bank in market efficiency: The case of Taiwan," Pacific-Basin Finance Journal, Elsevier, vol. 29(C), pages 239-260.
    10. León, Carlos & Machado, Clara & Sarmiento, Miguel, 2018. "Identifying central bank liquidity super-spreaders in interbank funds networks," Journal of Financial Stability, Elsevier, vol. 35(C), pages 75-92.
    11. Ivan Jericevich & Patrick Chang & Tim Gebbie, 2021. "Simulation and estimation of an agent-based market-model with a matching engine," Papers 2108.07806, arXiv.org, revised Aug 2021.
    12. Nina Karnaukh & Angelo Ranaldo & Paul Söderlind, 2015. "Understanding FX Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 28(11), pages 3073-3108.
    13. Nicolò Pecora & Pablo Rovira Kaltwasser & Alessandro Spelta, 2016. "Discovering SIFIs in Interbank Communities," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-17, December.
    14. Eduard Silantyev, 2019. "Order flow analysis of cryptocurrency markets," Digital Finance, Springer, vol. 1(1), pages 191-218, November.
    15. Pablo Rovira Kaltwasser & Alessandro Spelta, 2019. "Identifying systemically important financial institutions: a network approach," Computational Management Science, Springer, vol. 16(1), pages 155-185, February.
    16. Karl Finger & Daniel Fricke & Thomas Lux, 2013. "Network analysis of the e-MID overnight money market: the informational value of different aggregation levels for intrinsic dynamic processes," Computational Management Science, Springer, vol. 10(2), pages 187-211, June.
    17. Carlos León & Jhonatan Pérez & Luc Renneboog, 2014. "A multi-layer network of the sovereign securities market," Borradores de Economia 840, Banco de la Republica de Colombia.
    18. Ledenyov, Dimitri O. & Ledenyov, Viktor O., 2015. "Wave function method to forecast foreign currencies exchange rates at ultra high frequency electronic trading in foreign currencies exchange markets," MPRA Paper 67470, University Library of Munich, Germany.
    19. Lee, Seung Hwan, 2013. "Systemic liquidity shortages and interbank network structures," Journal of Financial Stability, Elsevier, vol. 9(1), pages 1-12.
    20. Chen, Tingqiang & Wang, Jiepeng & Liu, Haifei & He, Yuanping, 2019. "Contagion model on counterparty credit risk in the CRT market by considering the heterogeneity of counterparties and preferential-random mixing attachment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 520(C), pages 458-480.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:apmtfi:v:27:y:2020:i:6:p:520-548. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAMF20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.