Last week we read a lot about those guys at AIG who were paid $165 million in bonuses largely because (we were told) that, having screwed up the company, they were the only guys who could unscrew it.
Many of these people were bond traders. Others were “quants,” an occupation of whose existence most of us were blissfully unaware until last year, when the financial markets began to come unraveled. Stop any 20 people on any street in America not named Wall and odds are 19 of them couldn’t tell you what a quant does.
In that last sentence, you’ll note a question of probability – a 1 in 20 probability of knowing what a quant does, a figure I made up off the top of my head. What a quant would do is create a formula testing that probability, using complex mathematics, and then tell bond traders how to make money off of it.
Quant is short for quantitative analyst. In the ’80s and ’90s, they began to be all the rage on Wall Street. “A quant designs and implements mathematical models for the pricing of derivatives, assessments of risk or predicting market movements,” said Mark Joshi, a former quant at the Royal Bank of Scotland who has written several books on quants and how to become one.
At the risk of oversimplifying things, quants are the math nerds you used to beat up in high school. The world economic crisis is their revenge.
In my quest to understand quants, I am comforted by the fact that I was an A student in math in college – I made an A in freshman calculus some 40 years ago, having guessed right on the first test of the semester, whereupon the school burned down and no further tests were administered. I quit while I was ahead; I never took math again.
Still, I was able to fight my way through a fascinating piece by Felix Salmon in the Feb. 23 edition of Wired magazine in which he lays the blame for the financial collapse at the feet of a mathematician named David X. Li. In 2000, while working at JPMorgan Chase, Li published a paper in a financial journal. The paper was titled “On Default Correlation: A Copula Function Approach.”
I would explain his formula in all of its complexities except I know that many readers weren’t A students in college math. So here’s Felix Salmon’s explanation:
“For five years, Li’s formula, known as a Gaussian copula function (in math, ‘copula’ describes relationships between variables; Gauss was a 19th century mathematician), looked like an unambiguously positive breakthrough, a piece of financial technology that allowed hugely complex risks to be modeled with more ease and accuracy than ever before. With his brilliant spark of mathematical legerdemain, Li made it possible for traders to sell vast quantities of new securities, expanding financial markets to unimaginable levels.
“His method was adopted by everybody from bond investors and Wall Street banks to ratings agencies and regulators. And it became so deeply entrenched – and was making people so much money – that warnings about its limitations were largely ignored.”
The beauty of Li’s formula was that it appeared to take the risk out of risk. Traders no longer had to look at historic default patterns. Instead they just had to consider how prices of financial bets correlated over time. As prices boomed in the early 2000s, default correlations stayed low. Bonds could all be rated Triple A. The number of credit default swaps – in effect, bets on whether bonds would perform or not – went from $920 billion in 2001 to $62 trillion in 2007.
Nobody stopped to consider what would happen if the mortgages on which all these derivatives were based suddenly went south at the same time. Wall Street was like a horse player betting on a 10-race card filled with 20-1 to favorites. Who could imagine all 10 favorites breaking down on the same day?
This is why quants say they shouldn’t be blamed for the financial collapse: They just did the math. They didn’t say it was foolproof. Besides, if just a few traders had used Li’s formula, we wouldn’t be in this mess. The trouble started when it was adopted almost universally. Bankers and brokers were making so much money that no one wanted to stop.
What we need then are not fewer quants but better quants, people who can write a formula unraveling this mess without creating a bigger one. They’re the guys who ought to get the bonuses.
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