Monday, 29 December 2014

2014-114: Efficient Monte Carlo Counterparty Credit Risk Pricing and Measurement

Otmane El Rhazi, Samim Ghamami. 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. Full Text



Regards,

Otmane El Rhazi

Press Releases

Equity Trading

Text/Mobile, +44 7414 782 320





RISK DISCLOSURE STATEMENT

ANY OPINIONS, NEWS, RESEARCH, ANALYSIS, OR OTHER INFORMATION ON THIS WEBSITE IS PROVIDED AS GENERAL MARKET COMMENTARY ONLY. THERE ARE RISKS ASSOCIATED WITH UTILIZING AN INTERNET-BASED DEAL EXECUTION TRADING SYSTEM INCLUDING THE FAILURE OF HARDWARE, SOFTWARE, AND INTERNET CONNECTION.

No comments:

Post a Comment