Economic incentives and computer models at work

The WSJ reports on a certain casual attitude toward risk –- abetted by comforting historical financial models and economic incentives — that has apparently contributed to the recent turmoil in the mortgage and credit markets:

credit-rating firms also played a role in the subprime-mortgage boom that is now troubling financial markets. S&P, Moody’s Investors Service and Fitch Ratings gave top ratings to many securities built on the questionable loans, making the securities seem as safe as a Treasury bond…

since first asked to rate securities based on subprime loans more than a decade ago, they’ve done the best they could with the data they’ve had. “The housing market has proven to be weaker than a lot of expectations,” says Warren Kornfeld, co-head of residential mortgage-backed securities at Moody’s. This summer, the firms downgraded hundreds of mortgage bonds built on subprime mortgages. They say those bonds represent only a small part of the subprime mortgage market.

The subprime market has been lucrative for the credit-rating firms. Compared with their traditional business of rating corporate bonds, the firms get fees about twice as high when they rate a security backed by a pool of home loans…

In 2000, Standard & Poor’s made a decision about an arcane corner of the mortgage market. It said a type of mortgage that involves a “piggyback,” where borrowers simultaneously take out a second loan for the down payment, was no more likely to default than a standard mortgage….By 2006, S&P was making its own study of such loans’ performance. It singled out 639,981 loans made in 2002 to see if its benign assumptions had held up. They hadn’t. Loans with piggybacks were 43% more likely to default than other loans, S&P found.

In April 2006, S&P said it would raise by July the amount of collateral underwriters must include in many new mortgage portfolios. For instance, S&P could require that mortgage pools have extra loans in them, since it now expected a larger number to go bad. Still, S&P didn’t lower its ratings on existing securities, saying it had to further monitor the performance of loans backing them. It thus helped the market for these loans hold up through the end of 2006.

“the firms get fees about twice as high when they rate a security backed by a pool of home loans.” Well, that was certainly not a disincentive to looking with favor on the prospects for these securities.

Some borrowers didn’t care about credit and whether they could actually afford the properties they bought, particularly if they were buying properties to flip. Some originators didn’t care about credit — as long as there was a hungry market for their product. The banks who bought the loans were often similarly situated, as were some underwriters — if the market’s appetite for the securities was high, that was the important thing. Thus it fell to the rating agencies to be tasked with the function of rating default risks of portfolios whose characteristics often included no income verification, no down payment, and the unprecedented consequences of a rising rate reset environment starting from a very low base, among other factors.

The rating agencies used historical models to forecast future default rates. But those models came from a time before the flippers and other wise guys were in the game. This seems to us a little the internet IPO boom of the late 1990′s, as well as like Michael Milken’s analysis of junk bond default rates using the data prior to Drexel’s exploitation of this market inefficiency in the 1980′s. Hence, just as in those prior episodes, we have an unfavorable environment for those who bought securities whose value was based on faulty extrapolations from the past.

One Response to “Economic incentives and computer models at work”

  1. gs Says:

    The rating agencies used historical models to forecast future default rates. But those models came from a time before the flippers and other wise guys were in the game…we have an unfavorable environment for those who bought securities whose value was based on faulty extrapolations from the past.

    The notional value of the world’s over-the-counter derivatives was over $400 trillion in December 2006 while the gross market value was almost $10 trillion (HT: Wikipedia). $10 trillion controls a nominal $400 trillion in complicated, contradictory, and incompletely understood ways.

    The canonical models used to value derivatives assume that the underlying asset moves without regard to the existence of the derivative. Then, when the derivative is sold, the seller hedges by taking an offsetting position in the asset.

    And that’s just one of the issues with derivatives.

Leave a Reply