In Modeling Risk, the Human Factor Was Left Out - NYTimes.com
In Modeling Risk, the Human Factor Was Left Out - NYTimes.com
“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,”...
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.
“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,”...
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.
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