Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement

43 Pages Posted: 8 Jan 2015

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

Bo Zhang

IBM Corporation - Thomas J. Watson Research Center

Date Written: December 29, 2014

Abstract

Counterparty credit risk (CCR), a key driver of the 2007-08 credit crisis, has become one of the main focuses of the major global and U.S. regulatory standards. Financial institutions invest large amounts of resources employing Monte Carlo simulation to measure and price their counterparty credit risk. We develop efficient Monte Carlo CCR estimation frameworks by focusing on the most widely used and regulatory-driven CCR measures: expected positive exposure (EPE), credit value adjustment (CVA), and effective expected positive exposure (EEPE). Our numerical examples illustrate that our proposed efficient Monte Carlo estimators outperform the existing crude estimators of these CCR measures substantially in terms of mean square error (MSE). We also demonstrate that the two widely used sampling methods, the so-called Path Dependent Simulation (PDS) and Direct Jump to Simulation date (DJS), are not equivalent in that they lead to Monte Carlo CCR estimators which are drastically different in terms of their MSE.

Keywords: Basel II, Basel III, OTC derivatives market, Risk management, counterparty credit risk, credit value adjustment, efficient Monte Carlo simulation

JEL Classification: C00, G00

Suggested Citation

Ghamami, Samim and Zhang, Bo, Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement (December 29, 2014). FEDS Working Paper No. 2014-114, Available at SSRN: https://ssrn.com/abstract=2544692 or http://dx.doi.org/10.2139/ssrn.2544692

Samim Ghamami (Contact Author)

Securities and Exchange Commission (SEC) ( email )

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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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Bo Zhang

IBM Corporation - Thomas J. Watson Research Center ( email )

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Yorktown Heights, NY 10598
United States

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