"The Failure of Models that Predict Failure: Distance, Incentives and Defaults"


Statistical default models, widely used to assess default risk, are subject to a Lucas critique. We demonstrate this phenomenon using data on securitized subprime mortgages issued in the period 1997–2006. As the level of securitization increases, lenders have an incentive to originate loans that rate high based on characteristics that are reported to investors, even if other unreported variables imply a lower borrower quality. Consistent with this behavior, we find that over time lenders set interest rates only on the basis of variables that are reported to investors, ignoring other credit-relevant information. The change in lender behavior alters the data generating process by transforming the mapping from observables to loan defaults. To illustrate this effect, we show that a statistical default model estimated in a low securitization period breaks down in a high securitization period in a systematic manner: it under predicts defaults among borrowers for whom soft information is more valuable. Regulations that rely on such models to assess default risk may therefore be undermined by the actions of market participants.

 Gary Rogan comments:

Were the participants "allowed" to fail, sooner or later none of this would have been an issue. Were there not a government market for subprime loans to begin with this problem may have never developed in the first place. If the government didn't push for banks to make subrime loans this would be much slower to develop, if at all.

The government gave this problem the first push and then enabled, encouraged, and forced it all the way through. And that is how capitalism, red in tooth and claw, failed. Time to do some occupyin' of Wall Street.


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