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Why Risk Models Failed to Spot the Credit Crisis

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Why Risk Models Failed to Spot the Credit Crisis

Economy

Why Risk Models Failed to Spot the Credit Crisis

Why Risk Models Failed to Spot the Credit Crisis

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  • <iframe src="https://www.npr.org/player/embed/89507530/89507519" width="100%" height="290" frameborder="0" scrolling="no" title="NPR embedded audio player">
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Every big bank has a risk management team whose job it is to keep the banks out of trouble. The teams use complex computers to guide the banks away from financial danger. But as the global credit crisis shows, those models failed to keep many major U.S. and foreign financial firms from making bad bets on mortgages.

Andrew Aziz works for Algorithmics, which makes some of the more popular risk management software, especially AlgoRisk 2.3. It looks like Quicken. It has graphs and pie charts and spreadsheets.

But AlgoRisk functions more like a hysterical new parent, obsessively running through countless scary scenarios: What if there's a recession? What if interest rates fall too far or rise too fast, or the central bank of Kazakhstan collapses? It studies hundreds of thousands of these scenarios every day, trying to figure out how likely a bank is to lose a lot of money.

Yet for all it's worrying, the software completely failed to predict the current mortgage crisis.

Aziz says that to understand why the software failed, you have to understand the basic mathematics of risk management. And to do that, you have to go somewhere unexpected: a botanical garden.

Economic Lessons from Botany

Financial risk management uses a model discovered in 1827 by the Scottish botanist Robert Brown. He collected pollen and put it and some water under a microscope. He observed that the tiny pollen grains bounced around completely randomly. We now know that the grains were buffeted by constantly moving water molecules.

It turns out that what Brown discovered — called Brownian motion — is useful in studying all sorts of random phenomena, including financial risk. It's useful — but not foolproof.

Richard Lindsay is the author of How I Became a Quant, about financial engineers. He says financial models built on Brownian motion have done a great deal of good. These models have been so helpful in managing risk — at least some of the time — that they have made the world far richer. But that doesn't mean they always work — they're not perfect. But they're "the best thing that we have," Lindsay says.

Brownian motion works best with truly random things, like little bits of pollen that bounce around with no rhyme or reason. But financial markets are not random. They are linked. Risk management software is not always good at figuring out how those global links work.

Aziz, who helped write AlgoRisk 2.3, says the limitations of risk-management software are known. He compares the software to a GPS device used in a car: It's helpful as a guide, but the driver still needs to look out the window.

Aziz says some banks with a better understanding of Brownian motion and the vulnerabilities of the software planned correctly, and they are doing well now. Other banks just never looked out the window. And they crashed.

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