Algorithmics introduces the results of its latest performance benchmark carried out in conjunction with Intel at the end of 2008. The benchmark measured the computational performance of Algorithmics’ market risk and counterparty credit risk applications, including Algo Real-Time Credit Engine.
The test demonstrated that accurate credit exposure profiles can be calculated for a realistic over-the-counter derivative portfolio of 1 million trades, using a Monte Carlo scenario set with over 5,000 scenarios and 125 time steps in less than 2 hours on a single server using a two-socket, four-core, Intel Xeon processor 5400 series based on 45nm technology.
The benchmark test dataset contained a wide variety of derivative products including: interest rate swaps of various types; caps/floors; European, American and barrier options; and credit default swaps. The composition of the portfolio, maturity profile, number and range of risk factors, and complexity of netting and collateral agreements were chosen to represent a realistic trading portfolio of a Tier 1 trading bank.
The choice of 5,000 scenarios and 125 time steps represents an emerging best practice view from regulators and market practitioners of the number of scenarios and time steps that will be required in future to control sampling error and capture roll-off risk respectively.
For Algorithmics’ clients, the commercial impact of this benchmark promises to be twofold: lower capital and operating costs and, for the first time, very sophisticated risk assessment available real-time and pre-deal.
“By working closely with Intel and explicitly taking advantage of the Intel Xeon processor L5400, we have been able to get performance on a single server that ordinarily would require a large cluster with many hundreds of CPUs,” says Neil Bartlett, chief technology officer, Algorithmics. “This leads us to expect considerably lower capital and operating costs for our clients and makes Monte Carlo-based pre-deal credit exposure measurement and limits checking a practical and compelling alternative to more simplistic add-on approaches.”
“We are aware that risk management is now more than ever a priority across financial services,” says Nigel Woodward, global director, Financial Services, Intel. “These tests prove that where new investment is required to enable firms to prove they have effective policies in place, deep risk computation can be achieved on industry-standard, low-cost hardware.”
“This promises a cost-effective approach in terms of capital expenditures and ongoing operational costs due to lower energy consumption in the data center,” continues Nigel Woodward. “Using Intel’s fasterLAB, Intel’s engineers worked with Algorithmics to ensure optimized performance from the processor level in the architecture.”
“This benchmark demonstrates the strength of our innovation and R&D in improving software performance and reducing total cost of ownership for our clients,” says Michael Zerbs, president and COO, Algorithmics.
“Our breakthrough improvements in computational performance now mean that global institutions can cost-effectively perform full risk simulations for their largest trading counterparties in a few minutes instead of hours, and pre-deal, what-if risk profiles for plain vanilla and exotic derivatives at the transaction, portfolio, and counterparty levels can be completed in milli- or sub-seconds,” continues Michael Zerbs.
L.D.