During recent years the counterparty risk subject has received a growing
attention because of the so called Basel Accord. In particular the Basel III
Accord asks the banks to fulfill finer conditions concerning counterparty
credit exposures arising from banks' derivatives, securities financing
transactions, default and downgrade risks characterizing the Over The Counter
(OTC) derivatives market, etc. Consequently the development of effective and
more accurate measures of risk have been pushed, particularly focusing on the
estimate of the future fair value of derivatives with respect to prescribed
time horizon and fixed grid of time buckets. Standard methods used to treat the
latter scenario are mainly based on ad hoc implementations of the classic Monte
Carlo (MC) approach, which is characterized by a high computational time,
strongly dependent on the number of considered assets. This is why many
financial players moved to more enhanced Technologies, e.g., grid computing and
Graphics Processing Units (GPUs) capabilities. In this paper we show how to
implement the quantization technique, in order to accurately estimate both
pricing and volatility values. Our approach is tested to produce effective
results for the counterparty risk evaluation, with a big improvement concerning
required time to run when compared to MC approach.