Wall Street's failure of the Quants

NY Times:

Today’s economic turmoil, it seems, is an implicit indictment of the arcane field of financial engineering — a blend of mathematics, statistics and computing. Its practitioners devised not only the exotic, mortgage-backed securities that proved so troublesome, but also the mathematical models of risk that suggested these securities were safe.

What happened?

The models, according to finance experts and economists, did fail to keep pace with the explosive growth in complex securities, the resulting intricate web of risk and the dimensions of the danger.

But the larger failure, they say, was human — in how the risk models were applied, understood and managed. Some respected quantitative finance analysts, or quants, as financial engineers are known, had begun pointing to warning signs years ago. But while markets were booming, the incentives on Wall Street were to keep chasing profits by trading more and more sophisticated securities, piling on more debt and making larger and larger bets.

“Innovation can be a dangerous game,” said Andrew W. Lo, an economist and professor of finance at the Sloan School of Management of the Massachusetts Institute of Technology. “The technology got ahead of our ability to use it in responsible ways.”

That out-of-control innovation is reflected in the growth of securities intended to spread risk widely through the use of financial instruments called derivatives. Credit-default swaps, for example, were originally created to insure blue-chip bond investors against the risk of default. In recent years, these swap contracts have been used to insure all manner of instruments, including pools of subprime mortgage securities.

These swaps are contracts between two investors — typically banks, hedge funds and other institutions — and they are not traded on exchanges. The face value of the credit-default market has soared to an estimated $55 trillion.

Credit-default swaps, though intended to spread risk, have magnified the financial crisis because the market is unregulated, obscure and brimming with counterparty risk (that is, the risk that one embattled bank or firm will not be able to meet its payment obligations, and that trading with it will seize up).

The market for credit-default swaps has been at the center of the recent Wall Street banking failures and rescues, and these instruments embody the kinds of risks not easily captured in math formulas.

“Complexity, transparency, liquidity and leverage have all played a huge role in this crisis,” said Leslie Rahl, president of Capital Market Risk Advisors, a risk-management consulting firm. “And these are things that are not generally modeled as a quantifiable risk.”

Math, statistics and computer modeling, it seems, also fell short in calibrating the lending risk on individual mortgage loans. In recent years, the securitization of the mortgage market, with loans sold off and mixed into large pools of mortgage securities, has prompted lenders to move increasingly to automated underwriting systems, relying mainly on computerized credit-scoring models instead of human judgment.

So lenders had scant incentive to spend much time scrutinizing the creditworthiness of individual borrowers. “If the incentives and the systems change, the hard data can mean less than it did or something else than it did,” said Raghuram G. Rajan, a professor at the University of Chicago. “The danger is that the modeling becomes too mechanical.”

Mr. Rajan, a former chief economist at the International Monetary Fund, points to a new paper co-authored by a University of Chicago colleague, Amit Seru, “The Failure of Models That Predict Failure,” which looked at securitized subprime loans issued from 1997-2006. Their research concluded that the quantitative methods underestimated defaults for subprime borrowers in what the paper called “a systematic failure of default models.”

A recent paper by four Federal Reserve economists, “Making Sense of the Subprime Crisis,” found another cause. They surveyed the published research reports by Wall Street analysts and economists, and asked why the Wall Street experts failed to foresee the surge in subprime foreclosures in 2007 and 2008. The Fed economists concluded that the risk models used by Wall Street analysts correctly predicted that a drop in real estate prices of 10 or 20 percent would imperil the market for subprime mortgage-backed securities. But the analysts themselves assigned a very low probability to that happening.

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Projections are only as good as the underlying assumptions.

In this case the assumptions proved invalid so the formulas for risk analysis failed.

I am reminded of a discussion I had with an investment banker concerning the financial statements of a developer of a utility district. He felt the developers indemnification was all the brokerage firm needed to protect it from a potential default by the utility district. My response was that if the developer had the funds to pay the indemnification, he would pay his taxes to avoid default to begin with.

It always comes back to the borrowers ability to pay and that was lost in many of the mortgage deals because the persons making the lending decisions were shuffling the risk to others who were trying to avoid those risks with models with faulty assumptions. When the person making the lending decision has no concern about the borrowers potential default he can act irresponsibly without consequence. When its his companies money on the line and he is held responsible for his lending decision it sharpens the risk analysis at the source of the loan.