How safe are central counterparties in credit default swap markets?


We propose a general framework for estimating the vulnerability to default by a central counterparty (CCP) in the credit default swaps market. Unlike conventional stress testing approaches, which estimate the ability of a CCP to withstand nonpayment by its two largest counterparties, we study the direct and indirect effects of nonpayment by members and/or their clients through the full network of exposures. We illustrate the approach for the U.S. credit default swaps market under shocks that are similar in magnitude to the Federal Reserve’s stress tests. The analysis indicates that conventional stress testing approaches may underestimate the potential vulnerability of the main CCP for this market.

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Fig. 1

Source: Authors’ calculations, which use data provided by Markit Group Ltd.

Fig. 2

Source: Authors’ analysis

Fig. 3

Source: Authors’ calculations which use data provided to the OFR by The Depository Trust & Clearing Corporation and Markit Group Ltd.

Fig. 4

Source: Authors’ calculations, which use data provided to the OFR by The Depository Trust & Clearing Corporation and Markit Group Ltd.

Fig. 5

Source: Authors’ calculations, which use data provided to the OFR by The Depository Trust & Clearing Corporation and Markit Group Ltd.


  1. 1.

    For a further discussion of the effects of central clearing on systemic risk see [10, 16, 17, 26, 28, 30].

  2. 2.

    Poce et al. [27] study the Italian fixed income market instead of the derivatives market. They apply an exogenous shock to firms’ equity, estimate the impact on the assets of their counterparties using a Merton model, and then examine the impact on the CCP for the market in Italian government bonds (Cassa di Compensazione e Garanzia). Unlike the present study they do not have direct knowledge of firms’ network exposures, but must impute them. As in our study, however, they find that network contagion effects are substantial and imply a greater risk of CCP default than does the conventional Cover-2 standard.

  3. 3.

    The only other CCP in this market is CME Clearing, which in 2014 cleared less than 3% of the contracts and has since announced its exit from the market.

  4. 4.

    For more detail on the methodology underpinning these computations see [25].

  5. 5.

    This approach is consistent with CFTC Regulation 39.33(a) on the implementation of the Cover-2 standard, which assumes that the two largest BHCs default.

  6. 6.

    A similar model is used in Paddrik et al. [25] to analyze the extent to which the CCP contributes to network contagion.

  7. 7.

    In general, \(x \wedge y\) denotes the minimum of two real numbers x and y, and \([x]_+\) denotes the non-negative part of x.

  8. 8.

    Alternatively we could estimate the average transmission factor by the expression \((1/n) \sum _{i=0}^{n} (\bar{p}_i - p^*_i)/ s^*_i\). It can be shown that this yields a value at least as large as \(\tau ^*\) in (17).

  9. 9.

    We always assume that the CCP engages in variation margin gains haircutting (i.e., soft default) even when all other firms engage in hard default, due to the CCP’s contractual obligations to its members. Although \(\tilde{\Phi }(p)\) is not continuous in p, it is upper semicontinous and monotone decreasing in p, hence it has a greatest fixed point (see [25] for details).

  10. 10.

    In fact the U.S. authorities did not allow more than two large institutions (Lehman and Bear Stearns) to default during the recent financial crisis. The market implied default rates during the crisis also assigned a very low probability to four or more defaults [19].


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Correspondence to Mark Paddrik.

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We thank Randall Dodd, Marco Espinosa, Samim Ghamami, Dasol Kim, Bruce Tuckman, Stathis Tompaidis, Julie Vorman, and Bob Wasserman for their valuable comments. Additionally we would like to thank OFR’s ETL, Applications Development, Data Services, Legal, and Systems Engineering teams for collecting and organizing the data necessary to make this project possible. Views and opinions expressed are those of the authors and do not necessarily represent official positions or policy of the OFR or the U.S. Department of the Treasury.

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Paddrik, M., Young, H.P. How safe are central counterparties in credit default swap markets?. Math Finan Econ 15, 41–57 (2021).

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  • Credit default swaps
  • Central counterparties
  • Stress testing
  • Systemic risk
  • Financial networks

JEL Classification

  • D85
  • G01
  • G17
  • L14