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We’re still not much the wiser on exactly how the London Whale managed to lose $2 billion this quarter, but I think Matt Levine has the smartest take. (This is why the blogosphere is so great: it’s full of people who used to do this kind of thing for a living, rather than just people who write about people who do this for a living.)

The key thing to note here is that while the monster hit to the P&L is what got all the headlines, the real problem here lay with JPMorgan’s (JPM) risk models. A hint of far out of whack they are is given in the difference between the bank’s earnings release, which showed $67 million of value-at-risk in the Whale’s division in the first quarter, and the new SEC filing, which showed that number as actually being $129 million. Here’s Levine:

This was attributed to modeling changes made over the last year, and someone asked on the call “why did you change the VaR model?,” but I’m not convinced that’s exactly the right question. This, I suspect, is not an issue of a thing called a “VaR model” that sits in a central location and spits out numbers for regulators and 10-Qs; rather, this looks like the CIO’s trading desk modelling the actual P&L and risks of the trade wildly wrong. That seems to me like the simplest way to lose a billion dollars without noticing it.

I’d put this another way. JPMorgan’s Bruno Iksil, it seems, managed to find an incredibly profitable way of hedging the bank’s positions. Like any other economically rational actor, when he saw a lot of dollar bills lying on the sidewalk, he decided to pick them up. But in Iksil’s highly-complex world, a dollar bill isn’t really a dollar bill. Instead, it’s the output of a model. And if a trader can’t trust his model, he’s flying blind.

The problem is that pretty much by definition, it’s impossible to model model risk. We now know that Iksil’s model was deeply flawed. And indeed the minute that the rest of the world found out about his positions, they didn’t really pass the smell test: it’s very hard to see how writing an enormous amount of protection on an off-the-run CDX index would hedge anything much.

This is where grown-ups like Jamie Dimon are meant to step in. If they see billions of dollars in super-senior mortgage exposure, or in off-the-run CDX exposure, they’re meant to say “I know that your highfalutin’ models say that these exposures are risk free, but I don’t understand how this isn’t risky, so go unwind this trade”. Dimon has historically been very good at that — very good at refusing to simply trust that superstar traders earning eight-figure bonuses are doing nothing that might blow up in their faces. In this case, however, for some reason, he had blind faith in Iksil — and in Iksil’s models, which proved to be very faulty.

A modern trading desk is a bit like a high-tech airplane: nearly all of the time, you’re better off trusting your instruments than trusting your gut. But at the same time, if your instruments are broken, then trusting them can lead you to fly straight into the ocean.

This is why Basel I turned out to be much more robust than Basel II. Your sophisticated platform needs to be built on a foundation of dumb rules: simple limits on how big any one position can get, on how much exposure you can have to any one counterparty, or in general on any trade which is based on the hypothesis that your desk is smarter than anybody else on Wall Street.

Those kind of rules won’t prevent all blow-ups, of course, but they’ll help. They would have prevented this one, and they would have put an end to Jon Corzine’s disastrous MF Global trades, as well.

The problem is that traders hate dumb rules, because they cap the amount of money they can make. And traders have enormous power at investment banks these days, because they make the lion’s share of the profits. That’s why it’s important that the CEO of an investment bank not be a trader. And certainly it’s crucial that the CEO shouldn’t have his own trading account and buy and sell from his Blackberry during meetings, as Corzine did. That’s just a recipe for disaster.

Source: How Dumb Rules Can Mitigate Model Risk