Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement

Samim Ghamami and Bo Zhang

No 2014-114, Finance and Economics Discussion Series from Board of Governors of the Federal Reserve System (U.S.)

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 simulatio (search for similar items in EconPapers)
Pages: 43 pages
Date: 2014-12-17
New Economics Papers: this item is included in nep-ban and nep-rmg
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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