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Efficient Monte Carlo CVA Estimation

Proceedings of the 2014 Winter Simulation Conference

12 Pages Posted: 23 Dec 2014  

Samim Ghamami

Office of Financial Research, US Department of the Treasury; University of California, Berkeley - Center for Risk Management Research

Bo Zhang

IBM - T. J. Watson Research Center

Date Written: December 21, 2014

Abstract

This paper presents an overview of the efficient Monte Carlo counterparty credit risk (CCR) estimation framework recently developed by Ghamami and Zhang (2014). We focus on the estimation of credit value adjustment (CVA), one of the most widely used and regulatory-driven counterparty credit risk measures. Our proposed efficient CVA estimators are developed based on novel applications of well-known mean square error (MSE) reduction techniques in the simulation literature. Our numerical examples illustrate that the efficient estimators outperform the existing crude estimators of CVA substantially in terms of MSE.

Keywords: Risk Management, Counterparty Credit Risk, Credit Value Adjutment, Monte Carlo, Basel III

JEL Classification: C15, G1, G2

Suggested Citation

Ghamami, Samim and Zhang, Bo, Efficient Monte Carlo CVA Estimation (December 21, 2014). Proceedings of the 2014 Winter Simulation Conference. Available at SSRN: https://ssrn.com/abstract=2541346

Samim Ghamami (Contact Author)

Office of Financial Research, US Department of the Treasury ( email )

717 14th Street, NW
Washington, DC 20220
United States

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

581 Evans Hall
Berkely, CA 94720
United States

Bo Zhang

IBM - T. J. Watson Research Center ( email )

1101 Kitchawan Road, Route 134
Yorktown Heights, NY 10598
United States

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