Reval Reduces Time to Price OTC Derivatives

"Reval expanded a relationship with an Austin, Texas, firm so that it can more quickly price over-the-counter derivatives. The New York-based risk management and hedge accounting firm said it will implement SciFinance, a product of SciComp, a provider of automated pricing technology.

The code generation product allows users to develop derivatives pricing and risk models in-house. That will mean companies who use Reval's online accounting services of Reval's software will be able to cut the time it takes to value a portfolio of OTC derivatives.

Instead of needing one hour to conduct a Monte Carlo simulation, financial firms will need only a few seconds or a minute at most, say Reval executives.

Monte Carlo simulation is a common form of pricing complex OTC derivatives. Monte Carlo simulations require running between 20,000 to 50,000 trial sets of conditions and use a great deal of computer processing time. Implementing a Monte Carlo framework over the Web adds extra stress to computer processing times at month-end closing and at times of high system usage, says Reval.

The SciFinance product uses the Computer Unified Device Architecture and high-end graphical processing units (GPUs) from NVIDIA, a Santa Clara, Calif . company whose technology sprung up from the demands of video game play. That means that for Monte Carlo simulations, 50,000 instructions don't have to be sent down a computing path 50,000 times in a row. Instead, 100 core processors will take 500 each. The results then get collated.

"We were looking for a cost-effective and easy-to-deploy solution to improve the pricing of complex derivative instruments,'' said, Ernest Bonsell, senior vice president of product engineering at Reval.

Reducing the time it takes for firms to value their portfolios allows firms to calculate their market risk more quickly as well. About 400 corporations and financial firms use Reval to price their OTC derivatives contracts."

This article appeared in Securities Industry News.