Stochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA

19 Pages Posted: 13 Aug 2014

See all articles by Samim Ghamami

Samim Ghamami

Securities and Exchange Commission (SEC); New York University (NYU); University of California, Berkeley - Center for Risk Management Research

Lisa R. Goldberg

University of California, Berkeley; Aperio Group

Date Written: July 30, 2014

Abstract

Wrong way risk can be incorporated in Credit Value Adjustment (CVA) calculations in a reduced form model. Hull and White (2012) introduced a CVA model that captures wrong way risk by expressing the stochastic intensity of a counterparty's default time in terms of the financial institution's credit exposure to the counterparty. We consider a class of reduced form CVA models that includes the formulation of Hull and White and show that wrong way CVA need not exceed independent CVA. This result is based on some general properties of the model calibration scheme and a formula that we derive for intensity models of dependent CVA (wrong or right way). We support our result with a stylized analytical example as well as more realistic numerical examples based on the Hull and White model. We conclude with a discussion of the implications of our findings for Basel III CVA capital charges, which are predicated on the assumption that wrong way risk increases CVA.

Keywords: Credit value adjustment, stochastic intensity modeling, wrong way and right way risk, Basel III, counterparty credit risk

JEL Classification: G00, C00

Suggested Citation

Ghamami, Samim and Goldberg, Lisa R., Stochastic Intensity Models of Wrong Way Risk: Wrong Way CVA Need Not Exceed Independent CVA (July 30, 2014). FEDS Working Paper 2014-54, Available at SSRN: https://ssrn.com/abstract=2479520 or http://dx.doi.org/10.2139/ssrn.2479520

Samim Ghamami (Contact Author)

Securities and Exchange Commission (SEC) ( email )

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Washington, DC 20549-1105
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New York University (NYU) ( email )

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University of California, Berkeley - Center for Risk Management Research ( email )

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Lisa R. Goldberg

University of California, Berkeley ( email )

Department of Statistics
367 Evans Hall
Berkeley, CA 94720-3860
United States

Aperio Group ( email )

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Suite 315
Sausalito, CA 94965
United States

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