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Mathematical Problems in Engineering
Volume 2015, Article ID 959312, 6 pages
Research Article

Calculation of Credit Valuation Adjustment Based on Least Square Monte Carlo Methods

1School of Management, Harbin Institute of Technology, Harbin 150001, China
2School of Finance, Harbin University of Commerce, Harbin 150028, China

Received 16 June 2014; Accepted 24 November 2014

Academic Editor: Mohamed A. Seddeek

Copyright © 2015 Qian Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Counterparty credit risk has become one of the highest-profile risks facing participants in the financial markets. Despite this, relatively little is known about how counterparty credit risk is actually priced mathematically. We examine this issue using interest rate swaps. This largely traded financial product allows us to well identify the risk profiles of both institutions and their counterparties. Concretely, Hull-White model for rate and mean-reverting model for default intensity have proven to be in correspondence with the reality and to be well suited for financial institutions. Besides, we find that least square Monte Carlo method is quite efficient in the calculation of credit valuation adjustment (CVA, for short) as it avoids the redundant step to generate inner scenarios. As a result, it accelerates the convergence speed of the CVA estimators. In the second part, we propose a new method to calculate bilateral CVA to avoid double counting in the existing bibliographies, where several copula functions are adopted to describe the dependence of two first to default times.