I linked to Felix Salmon's article on the formula that murdered Wall Street in a tab dump last week, but I want to emphasize a bit more. Salmon explores what is, I think, the weirdest part of the meltdown: How the risk estimates -- the very analyses meant to predict catastrophe -- became fantasy numbers. He focuses on an estimation model developed by a mathematician named David Li. It's hard to estimate risk in the real world. Major chains of default and catastrophe are rare which means that there are few data points you can use to build out patterns. But Li came up with a simple and ingenious solution: He used changes in the credit default market to estimate risk correlations. Rather than real world defaults, in other words, he used the market's estimation of the likelihood of real world default. It spit out a clean number that could then be used to package assets. The massive assumption here was that past CDS data would correctly estimate future risk. It didn't. More than that: There was no real reason to believe that it would. Salmon continues:Bankers should have noted that very small changes in their underlying assumptions could result in very large changes in the correlation number. They also should have noticed that the results they were seeing were much less volatile than they should have been—which implied that the risk was being moved elsewhere. Where had the risk gone?They didn't know, or didn't ask. One reason was that the outputs came from "black box" computer models and were hard to subject to a commonsense smell test. Another was that the quants, who should have been more aware of the copula's weaknesses, weren't the ones making the big asset-allocation decisions. Their managers, who made the actual calls, lacked the math skills to understand what the models were doing or how they worked. They could, however, understand something as simple as a single correlation number. That was the problem."The relationship between two assets can never be captured by a single scalar quantity," Wilmott says. For instance, consider the share prices of two sneaker manufacturers: When the market for sneakers is growing, both companies do well and the correlation between them is high. But when one company gets a lot of celebrity endorsements and starts stealing market share from the other, the stock prices diverge and the correlation between them turns negative. And when the nation morphs into a land of flip-flop-wearing couch potatoes, both companies decline and the correlation becomes positive again. It's impossible to sum up such a history in one correlation number, but CDOs were invariably sold on the premise that correlation was more of a constant than a variable.
The question that Salmon doesn't engage is why they didn't ask and felt no need to know. And the answer, I'd submit, is that the simplest interpretation of Li's number was the best for business. As Upton Sinclair used to say, it is difficult to get a man to understand something when his salary depends upon his not understanding it. It's even harder to get him to question something when his bonus depends on not questioning it. Li's formula is now rather discredited. But there will be another. You can't prevent greed through regulation. You can, however, keep the consequences from doing more than ruining a couple portfolios. These investment decisions could only imperil the broader economy because they were so highly leveraged. Limit the available leverage and you contain the possible damage.
