Reval is a leading derivative risk management and hedge accounting software-as-a-service (SaaS) provider. In order to quickly develop new structured products and vastly speed up Monte Carlo based derivatives for its SaaS customers, Reval chose SciFinance® from SciComp, used in conjunction with GPU hardware from NVIDIA.
Reval has been adding complex structured instruments to it's flagship SaaS product, Reval®. These instruments included Dual Currency bonds, Power Reverse Dual Currency bonds, Inverse Floaters and CMS Steepeners, all with embedded caps and floors and call/put options as well as Range Accruals and Principal Protected Notes. Reval needed to find a way to improve the development time for these instruments.
These types of derivatives are priced using Monte Carlo simulation, which requires running between 20,000 to 50,000 trials and uses a great deal of CPU time. Implementing a Monte Carlo framework in a SaaS environment adds extra stress to CPU usage at month-end closing and at times of high system usage. A 50,000 trial simulation of a complex instrument can take up to 60 seconds to run in a conventional hosted environment. Even with dedicated analytics servers, this can cause unacceptable response times as analytics servers are shared across hundreds of clients.
Reval teamed with SciComp to implement a combined hardware and software solution. The results were a faster derivatives model development cycle and accelerations of 100X faster than serial code for derivative valuations in the SaaS environment.
SciFinance® automates pricing and risk model development
SciPDE™ and SciMC™ are the core SciFinance modules
SciGPU™ achieves blazing fast performance with CUDA and OpenMP
SciCalibrator™ provides pricing model calibration
SciIntegrator™ eases integration
Standalone customizable pricing and calibration tools.
Derivatives Pricing Models
A resource site with examples, documentation and more...
16 - 20 April 2012, Hotel Arts Barcelona
Software vendors and service providers ease GPU adoption
...this approach masks the complexity of parallel programming from the end user, leaving them free to define the characteristics of the pricing model that they want to run on GPUs.