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 associated with a counterparty default before the end of an OTC contract.
Several CCR measures are in use in practice, e.g., credit valuation 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.
This white paper examines the implementation of CVA algorithms. It outlines their typical structure and explains how the increasing complexity in the algorithms consumes vast computing resources. It is shown how such algorithms are typically implemented in C++ and executed in a grid infrastructure, before introducing how great speed-ups can be achieved. Using a real-world case study from a tier-one investment bank, the paper demonstrates how performance can be doubled on an existing CPU grid infrastructure and how a ten-fold speed-up can be achieved with a combination of CPU and GPU resources. Such speedups are achieved while keeping the developer productivity high, cutting the total cost of ownership.
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