Institutional Investor's Alpha Magazine - February 2008 - (Page 55)

The complex nature of over-the-counter derivatives, in which the details are agreed upon by our counterparties at the time of a trade, lends itself well to a robust technology solution. All the details must be checked by someone in the middle ofﬁce and back ofﬁce, and we can do that through a combination of manual and electronic eyes. We input the details into our system, the other side inputs the details into their system, and wherever possible we use electronic matching to make sure the details match. There are large organizations and platforms we utilize to do that, but we have to make those integrations in the ﬁrst place. Similarly, for credit there is a spread curve for every single name in the credit market — thousands of curves. Loading them into your system, ensuring data integrity and making sure all the curves are accurate requires a fair bit more analysis and automatic data scrubbing. If a trader is looking at a curve used to price a certain instrument, or an operations guy is looking at it, they need the same tools and data. Technology is really the only way to manage that volume of information. What are the technology challenges in risk analysis, and how do you deal with them? market gyrations. There is seldom much of a lag between what our models kick out and what the market is doing. Even if things are gyrating, we can weather the storm and keep up to date on what the true market prices are. The recent market gyrations weren’t caused as much by models that didn’t stand up well, as by people using models inappropriately. Any model just does what it is told. It’s up to the trader, the analyst and the risk manager to understand what the model is telling them and to know its limitations. A lot of desks out there simply don’t have that expertise and are treating these models as a black box. That is a dangerous thing. When you say some people treat it as a black box and your people don’t — what exactly does that mean? Our technology strategy includes a few approaches. First, we maintain rigorous calibration for our analytics. We calibrate to various sources, including third-party sources and our actual counterparty evaluations — on a daily basis. That is a key way to make sure that the way we quantify risk internally matches what the market does. The second piece is grid technology — which is essentially a fancy way of taking applications and chunks of code and running them remotely on computers in parallel fashion. You are leveraging assets you already have that are idle. In order to really get a handle on your risk, you need to run scenarios and do stress analysis and regression analysis. To do that requires an awful lot of computational work. Grid technology allows us to take advantage of all of our hardware and to really assure our investors that we have looked at all the scenarios of concern; the grid is, in my mind, the most cost-efﬁcient way to do that. How have these changes affected your ability to do modeling and analysis? Let’s say I buy my models from a third-party vendor. I am a tranche trader, and I am looking at a tranche of a subprime-backed CDO. I want to buy a certain amount of a senior tranche. What should I pay for that risk? They punch it into the model and say, “Okay, that is the price.” If it’s a black box, that trader may not know what assumptions the model made. Did it make assumptions about the homogeneity of the spreads inside the basket? Did it make assumptions about the probability of default over time? If you go to any of the risk conferences, any of the forums or discussions, the development of these models is still an ongoing process. At the end of the day, the truth is what the market will pay. That is where calibration comes in. That is where our continuous feedback ensures that our models are as accurate as possible. So it all boils down to having a very robust modeling system? We can get risk to the users more quickly — whether a risk manager, a trader, an accountant or an operations desk — allowing them to do things on a more ad hoc basis. If they want to run a big simulation instead of having to wait all night or kick it off before they leave for the weekend, they can get results in a few minutes. How do you use technology to model and price assets, and how did these systems hold up in the recent volatile market? Not only having a robust system, but also having traders who understand the models well enough to know their limitations. And second is the feedback, not just assuming that your models are always right. I might be stubborn and think, “The market is pricing that wrong, my model is right.” If Goldman Sachs, Merrill Lynch and Deutsche Bank will pay only so much for it, then, guess what, your model’s wrong. Maybe someday they will agree with you. But if you have mark-to-market exposure and you have margin calls, you can ﬁnd yourself in trouble before the market corrects and comes back to your model. It is all about robust models, traders who understand the models and having calibration feedback. What is the challenge of bringing these new systems in without disruption? Our portfolios include instruments priced on models, and we use a combination of models developed in-house as well as commercial models. They stood up quite nicely to the We test everything we build thoroughly. We’re fortunate that the end users of our software are our employees, which makes it easier to get it right the ﬁrst time. It requires more effort than sticking with the status quo, but I doubt anyone here would want to switch back to the legacy system. FEBRUARY 2008 • INSTITUTIONAL INVESTOR’S ALPHA • 55

Table of Contents Feed for the Digital Edition of Institutional Investor's Alpha Magazine - February 2008

Institutional Investor's Alpha Magazine - February 2008 Contents Letter from the Editor Longs & Shorts Pension Corner: Alternative Perspective The Good Guys: A Simple Plan Cover Story: Energy and Commodities: Fueling Opportunity Regulation: Energy Trading: The Brawl on the Hill Interview: Master of Values Regulation: Hedge Fund Registration: Way Out West Research Center: Top Hedge Fund Analysts Alpha Bytes: Rocket Man Unhedged: Commentary: Structured for Success

Institutional Investor's Alpha Magazine - February 2008

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