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Bounding Wrong-Way Risk in Measuring Counterparty Risk

Author

Listed:
  • Paul Glasserman

    (Office of Financial Research
    Columbia University)

  • Linan Yang

    (Columbia University)

Abstract

Counterparty risk measurement integrates two sources of risk: market risk, which determines the size of a firm's exposure to a counterparty, and credit risk, which reflects the likelihood that the counterparty will default on its obligations. Wrong-way risk refers to the possibility that a counterparty’s default risk increases with the market value of the exposure. We investigate the potential impact of wrong-way risk in calculating a credit valuation adjustment (CVA) to a derivatives portfolio: CVA has become a standard tool for pricing counterparty risk and setting associated capital requirements. We present a method, introduced in our earlier work, for bounding the impact of wrong-way risk on CVA. The method holds fixed marginal models for market and credit risk while varying the dependence between them. Given simulated paths of the two models, we solve a linear program to find the worst-case CVA resulting from wrongway risk. The worst case can be overly conservative, so we extend the procedure by penalizing deviations of the joint model from a baseline model. By varying the penalty for deviations, we can sweep out the full range of possible CVA values for different degrees of wrong-way risk. Our method addresses an important source of model risk in counterparty risk measurement.

Suggested Citation

  • Paul Glasserman & Linan Yang, 2015. "Bounding Wrong-Way Risk in Measuring Counterparty Risk," Working Papers 15-16, Office of Financial Research, US Department of the Treasury.
    • Handle: RePEc:ofr:wpaper:15-16
    as

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    File URL: https://financialresearch.gov/working-papers/files/OFRwp-2015-16_Wrong-Way-Risk-in-Measuring-Counterparty-Risk.pdf
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    References listed on IDEAS

    as
    1. Friedrich Pukelsheim, 2014. "Biproportional scaling of matrices and the iterative proportional fitting procedure," Annals of Operations Research, Springer, vol. 215(1), pages 269-283, April.
    2. Damiano Brigo & Agostino Capponi & Andrea Pallavicini, 2014. "Arbitrage-Free Bilateral Counterparty Risk Valuation Under Collateralization And Application To Credit Default Swaps," Mathematical Finance, Wiley Blackwell, vol. 24(1), pages 125-146, January.
    3. Samim Ghamami & Lisa R. Goldberg, 2014. "Stochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA," Finance and Economics Discussion Series 2014-54, Board of Governors of the Federal Reserve System (U.S.).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance Part 2: Wrong Way Funding Risk," Papers 1910.03993, arXiv.org.
    2. Derek Singh & Shuzhong Zhang, 2020. "Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk," Applied Economics and Finance, Redfame publishing, vol. 7(6), pages 70-100, December.
    3. Janis Müller & Peter N. Posch, 2018. "Wrong-way-risk in tails," Journal of Asset Management, Palgrave Macmillan, vol. 19(4), pages 205-215, July.
    4. Derek Singh & Shuzhong Zhang, 2019. "Distributionally Robust XVA via Wasserstein Distance: Wrong Way Counterparty Credit and Funding Risk," Papers 1910.01781, arXiv.org, revised May 2020.

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