Bounding Wrong-Way Risk in Measuring Counterparty Risk

16 Pages Posted: 22 Aug 2015

See all articles by Paul Glasserman

Paul Glasserman

Columbia Business School

Linan Yang

Columbia University - Department of Industrial Engineering and Operations Research (IEOR)

Date Written: August 19, 2015

Abstract

Counterparty risk measurement integrates two sources of risk: market risk, which determines the size of a firm’s exposure to a counter party, 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 wrong way 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.

Keywords: credit valuation adjustment, counterparty credit risk, wrong-way risk, iterative

Suggested Citation

Glasserman, Paul and Yang, Linan, Bounding Wrong-Way Risk in Measuring Counterparty Risk (August 19, 2015). Office of Financial Research Working Paper No. 15-16, Columbia Business School Research Paper No. 15-76, Available at SSRN: https://ssrn.com/abstract=2648495 or http://dx.doi.org/10.2139/ssrn.2648495

Paul Glasserman (Contact Author)

Columbia Business School ( email )

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Linan Yang

Columbia University - Department of Industrial Engineering and Operations Research (IEOR) ( email )

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New York, NY 10027
United States

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