Accelerating CVA Computations the Easy Way
The risks associated with over-the-counter (OTC) derivatives were the key contributing factor to the 2007/2008 global financial crisis. Therefore financial institutions worldwide have drastically shifted the focus of their risk management
towards counterparty credit risk (CCR), i.e., the risk that a counterparty defaults before the end of an OTC contract.
Several CCR measures are in use in practice, e.g., credit value adjustment (CVA), and international regulatory frameworks (i.e. Basel III) introduced more measures and increased the computational complexity. In addition, banks are
striving to quickly respond to market and regulatory changes through flexible in-house software. This makes fast and maintainable software implementations essential.
Monte-Carlo Methods Acceleration
Monte-Carlo simulations are among the most common numerical methods in computational finance. They are used when closed form solutions or other numerical methods are not practical or do not exist. Complex financial problems are often evaluated numerically using Monte-Carlo methods. For example, Monte-Carlo derivative pricing determines the current value based on a large number of random experiments and some form of statistical analysis to obtain the results.
This white paper demonstrates the benefits of using the Xcelerit platform to efficiently run Monte-Carlo simulations using multi-core CPUs and graphics processing units (GPUs). The generic approach for Monte-Carlo simulations using the Xcelerit platform is presented and performance figures are given for the specific examples of pricing European options and pricing a portfolio of LIBOR swaptions.