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Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement

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Listed:
  • Samim Ghamami
  • Bo Zhang

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.

Suggested Citation

  • Samim Ghamami & Bo Zhang, 2014. "Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement," Finance and Economics Discussion Series 2014-114, Board of Governors of the Federal Reserve System (U.S.).
    • Handle: RePEc:fip:fedgfe:2014-114
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    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Samim Ghamami, 2013. "Counterparty Credit Risk," Quantitative Finance, Taylor & Francis Journals, vol. 13(12), pages 1863-1865, December.
    3. Paul Glasserman & Philip Heidelberger & Perwez Shahabuddin, 1999. "Asymptotically Optimal Importance Sampling and Stratification for Pricing Path‐Dependent Options," Mathematical Finance, Wiley Blackwell, vol. 9(2), pages 117-152, April.
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    Cited by:

    1. Yuriy Krepkiy & Asif Lakhany & Amber Zhang, 2021. "Efficient Least Squares Monte-Carlo Technique for PFE/EE Calculations," Papers 2105.07061, arXiv.org.
    2. Samim Ghamami, 2015. "Static models of central counterparty risk," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 2(02), pages 1-36.

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