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.
The miss by Wall Street analysts shows how models can be precise out to several decimal places, and yet be totally off base. The analysts, according to the Fed paper, doggedly clung to the optimists’ mantra that nominal housing prices in the United States had not declined in decades — even though house prices did fall nationally, adjusted for inflation, in the 1970s, and there are many sizable regional declines over the years.
Besides, the formation of a housing bubble was well under way. Until 2003, prices moved in line with employment, incomes and migration patterns, but then they departed from the economic fundamentals.
The Wall Street models, said Paul S. Willen, an economist at the Federal Reserve in Boston, included a lot of wishful thinking about house prices. But, he added, it is also true that asset price trends are difficult to predict. “The price of an asset, like a house or a stock, reflects not only your beliefs about the future, but you’re also betting on other people’s beliefs,” he observed. “It’s these hierarchies of beliefs — these behavioral factors — that are so hard to model.”
Indeed, the behavioral uncertainty added to the escalating complexity of financial markets help explain the failure in risk management. The quantitative models typically have their origins in academia and often the physical sciences. In academia, the focus is on problems that can be solved, proved and published — not messy, intractable challenges. In science, the models derive from particle flows in a liquid or a gas, which conform to the neat, crisp laws of physics.
Not so in financial modeling. Emanuel Derman is a physicist who became a managing director at Goldman Sachs, a quant whose name is on a few financial models and author of “My Life as a Quant — Reflections on Physics and Finance” (Wiley, 2004). In a paper that will be published next year in a professional journal, Mr. Derman writes, “To confuse the model with the world is to embrace a future disaster driven by the belief that humans obey mathematical rules.”
Yet blaming the models for their shortcomings, he said in an interview, seems misguided. “The models were more a tool of enthusiasm than a cause of the crisis,” said Mr. Derman, who is a professor at Columbia University.
In boom times, new markets tend to outpace the human and technical systems to support them, said Richard R. Lindsey, president of the Callcott Group, a quantitative consulting group. Those support systems, he said, include pricing and risk models, back-office clearing and management’s understanding of the financial instruments. That is what happened in the mortgage-backed securities and credit derivatives markets.
Better modeling, more wisely applied, would have helped, Mr. Lindsey said, but so would have common sense in senior management. The mortgage securities markets, he noted, grew rapidly and generated high profits for a decade. “If you are making a high return, I guarantee you there is a high risk there, even if you can’t see it,” said Mr. Lindsey, a former chief economist of the Securities and Exchange Commission.
Among quants, some recognized the gathering storm. Mr. Lo, the director of M.I.T. Laboratory for Financial Engineering, co-wrote a paper that he presented in October 2004 at a National Bureau of Economic Research conference. The research paper warned of the rising systemic risk to financial markets and particularly focused on the potential liquidity, leverage and counterparty risk from hedge funds.
Over the next two years, Mr. Lo also made presentations to Federal Reserve officials in New York and Washington, and before the European Central Bank in Brussels. Among economists and academics, he said, the research was well received. “On the industry side, it was dismissed,” he recalled.
The dismissive response, Mr. Lo said, was not really surprising because Wall Street was going to chase profits in the good times. The path to sensible restraint, he said, will include not only better risk models, but also more regulation. Like others, Mr. Lo recommends higher capital requirements for banks and the use of exchanges or clearinghouses for the trade of exotic securities, so that prices and risks are more visible. Any hedge fund with more than $1 billion in assets, he added, should be compelled to report its holdings to regulators.
Financial regulation, Mr. Lo said, should be seen as similar to fire safety rules in building codes. The chances of any building burning down are slight, but ceiling sprinklers, fire extinguishers and fire escapes are mandated by law.
“We’ve learned the hard way that the consequences can be catastrophic, even if statistically improbable,” he said.