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Q&A: Grid in the financial industry
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April 26, 2005 (Computerworld) -- While mainstream enterprise IT professionals still regard grid computing as a nascent technology, the financial services vertical market has been using grid and the open-source Globus Toolkit in the production environment for years (see story). In financial services, the question isn't so much who uses grid, but who doesn't.
This month, I spoke with Deborah Williams, who leads the capital markets practice at industry research firm Financial Insights, an IDC company (owned by Computerworld's parent company, IDG). Williams has had a great vantage point on the evolution of IT in institutional trading, retail brokerages and risk management. I thought it would be interesting to hear her views about why grid has been such a natural fit in financial services.
Tell us a bit about the computing evolution in financial services. It's the same compute evolution that everyone else has followed.
The first step was the monolithic mainframe with lots of distributed dumb terminals. There was a lot of distributed access, and then came the development of client/server and LAN-based systems that distributed actual application logic. Then you had the data distributed through N-tier architectures. Chunks of code that ran more slowly or [were] compute-intensive were offloaded onto another tier -- that evolution has been happening for a while.
Where does grid fit in financial services? Financial services has a long history of doing most of its heavy lifting in overnight batch cycles. Most of what drives the need for distributed computing is whatever they need on an ad hoc basis. In capital markets, it might be pricing or scenario analysis -- those types of activities that help traders monitor whether they should be buying or selling.
Where you see grid applied most often is in areas like risk management or middle-office kinds of functions, where even an eight-hour batch cycle wasn't enough to run re-evaluations on a whole portfolio. Not too long ago, banks were running 22-hour batch cycles, but that became pretty useless. The dealing room is open a significant portion of that time, so by the time you got that report 48 hours later, it was already outdated. As the instruments and the methodology for looking at risk scenarios [like Monte Carlo simulations, see story] became more complex, it became clear that they needed to do a better job of keeping up.
Monte Carlo simulations are used in financial services to do randomized scenario analysis. It's become very popular for risk management. You can say, "We know the prices over the last x years look like this, so let's throw at this thing all the possible parameters." You end up with a distribution of prices.
If you assume a bell-shaped distribution curve, the traders care about the down-side tail. They care about whether it's flat, or tails back up -- so they're looking the shape of that tail for extreme risks. Monte Carlo analysis allows you to get a much better sense of what the tail looks like, rather than just predetermined scenarios.
In order to do this correctly, one must run ideally a minimum of 10,000 different re-evaluations to get enough points on the curve to project what the curve looks like.
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