42 Pages Posted: 19 Sep 2013 Last revised: 23 Jul 2014
Date Written: July 23, 2014
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: Counterparty credit risk (CCR), central counterparty credit risk, regulatory CCR measures, credit value adjustment, efficient Monte Carlo
JEL Classification: G1, C15
Suggested Citation: Suggested Citation
Ghamami, Samim and Zhang, Bo, Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement (July 23, 2014). Available at SSRN: https://ssrn.com/abstract=2327562 or http://dx.doi.org/10.2139/ssrn.2327562