Hypothetical Risk Valuation System

IT systems of banks and investment funds are versatile. They have been grown for years and today financial institutions increasingly rely on the quality of their quantitative technology. Such a trend has made computational finance topmost important.

Computational finance is a cross-disciplinary field that focuses on the financial services industry and relies on mathematical finance, numerical methods and computer simulations to make investment decisions and facilitates portfolio risk management.

Despite the fact banks tend to develop risk valuation systems in-house all of them can generally fit into the given below schema.

How the system works

JP Morgan Heads HPC

JP Morgan is now able to run risk analysis and price its global credit portfolio in near real-time after implementing High Performance Computing (HPC) capabilities.
Prior to the implementation, JP Morgan would take eight hours to do a complete risk run, and an hour to run a present value, on its entire book. If anything went wrong with the analysis, there was no time to re-run it. It has now reduced that to about 238 seconds, with an FPGA time of 12 seconds.
Read full article here

Parallel Programming for Quantitative Finance

Investment banks like calculations that require powerful computational resources. In many cases Monte-Carlo simulations are run on huge GRID systems that cost a lot.
Such systems are usually home grown and really look expensive to replicate elsewhere.

Few monthes ago I read an article in RISK magazine that outlined the same problem and as an option a  chipper and more flexible approach was mentioned that is based on multi-core CPU and GPU.
http://www.nvidia.co.uk/content/EMEAI/PDF/risk-magazine-tesla-april2013.pdf

GPU programming probably is not too complex although it definitely requires some background desk devs or quants might not have. A company Xcelerit http://www.xcelerit.com/ made an attempt to ease the parallel programming. They provide SDK that allows quants develop and execute their C++ programs on a high-performance environment.

Furthermore they have a library that comes with base statistics functions, market data adapters and a number of interfaces for commonly used software packages, e.g. MATLAB, Excel.
http://www.xcelerit.com/xcelerit-forges-new-tools-for-quantitative-finance/

They also outlined some case studies of inefficiencies of sequential program execution on a GRID compared to multi-core CPU architectures.
http://blog.xcelerit.com/efficiently-using-computing-grids-in-the-financial-industry/

Examples of SDK usage
http://blog.xcelerit.com/xcelerit-sdk-user-experience/

HSBC usage
http://blog.xcelerit.com/hsbc-run-risk-in-real-time-with-xcelerit-and-gpus/

Let's see whether this attempt of Xcelerit will have a successful continuation.