In Nature’s Casino
Aug 26, 2007
August 26, 2007
In Nature’s Casino
By MICHAEL LEWIS
It was Aug. 24, 2005, and New Orleans was still charming. Tropical Depression 12 was spinning from the Bahamas toward Florida, but the chances of an American city’s being destroyed by nature were remote, even for one below sea level. An entire industry of weather bookies — scientists who calculate the likelihood of various natural disasters — had in effect set the odds: a storm that destroys $70 billion of insured property should strike the United States only once every 100 years. New Orleanians had made an art form of ignoring threats far more likely than this; indeed, their carelessness was a big reason they were supposedly more charming than other Americans. And it was true: New Orleanians found pleasure even in oblivion. But in their blindness to certain threats, they could not have been more typically American. From Miami to San Francisco, the nation’s priciest real estate now faced beaches and straddled fault lines; its most vibrant cities occupied its most hazardous land. If, after World War II, you had set out to redistribute wealth to maximize the sums that might be lost to nature, you couldn’t have done much better than Americans had done. And virtually no one — not even the weather bookies — fully understood the true odds.
But there was an exception: an American so improbably prepared for the havoc Tropical Depression 12 was about to wreak that he might as well have planned it. His name was John Seo, he was 39 years old and he ran a hedge fund in Westport, Conn., whose chief purpose was to persuade investors to think about catastrophe in the same peculiar way that he did. He had invested nearly a billion dollars of other people’s money in buying what are known as “cat bonds.” The buyer of a catastrophe bond is effectively selling catastrophe insurance. He puts down his money and will lose it all if some specified bad thing happens within a predetermined number of years: a big hurricane hitting Miami, say, or some insurance company losing more than $1 billion on any single natural disaster. In exchange, the cat-bond seller — an insurance company looking to insure itself against extreme losses — pays the buyer a high rate of interest.
Whatever image pops to mind when you hear the phrase “hedge fund manager,” Seo (pronounced so) undermines it. On one hand, he’s the embodiment of what Wall Street has become: quantitative. But he’s quirky. Less interested in money and more interested in ideas than a Wall Street person is meant to be. He inherited not money but math. At the age of 14, in 1950, his mother fled North Korea on foot, walked through live combat, reached the United States and proceeded to become, reportedly, the first Korean woman ever to earn a Ph.D. in mathematics. His father, a South Korean, also came to the United States for his Ph.D. in math and became a professor of economic theory. Two of his three brothers received Ph.D.’s — one in biology, the other in electrical engineering. John took a physics degree from M.I.T. and applied to Harvard to study for his Ph.D. As a boy, he says, he conceived the idea that he would be a biophysicist, even though he didn’t really know what that meant, because, as he puts it, “I wanted to solve a big problem about life.” He earned his doctorate in biophysics from Harvard in three years, a department record.
His parents had raised him to think, but his thoughts were interrupted once he left Harvard. His wife was pregnant with their second child, and the health plan at Brandeis University, where he had accepted a job, declared her pregnancy a pre-existing condition. He had no money, his parents had no money, and so to cover the costs of childbirth, he accepted a temp job with a Chicago trading firm called O’Connor and Associates. O’Connor had turned a small army of M.I.T. scientists into options traders and made them rich. Seo didn’t want to be rich; he just wanted health insurance. To get it, he agreed to spend eight weeks helping O’Connor price esoteric financial options. When he was done, O’Connor offered him 40 grand and asked him to stay, at a starting salary of $250,000, 27 times his post-doc teaching salary. “Biophysics was starved for resources,” Seo says. “Finance was hurling resources at problems. It was almost as if I was taking it as a price signal. It was society’s way of saying, Please, will you start solving problems over here?”
His parents, he suspected, would be appalled. They had sacrificed a lot for his academic career. In the late 1980s, if you walked into the Daylight Donuts shop in Dallas, you would have found a sweet-natured Korean woman in her early 50s cheerfully serving up honey-glazed crullers: John’s mom. She had abandoned math for motherhood, and then motherhood for doughnuts, after her most promising son insisted on attending M.I.T. instead of S.M.U., where his tuition would have been free. She needed money, and she got it by buying this doughnut shop and changing the recipe so the glaze didn’t turn soggy. (Revenues tripled.) Whatever frustration she may have felt, she hid, as she did most of her emotions. But when John told her that he was leaving the university for Wall Street, she wept. His father, a hard man to annoy, said, “The devil has come to you as a prostitute and has asked you to lie down with her.”
A willingness to upset one’s mother is usually a promising first step to a conventional Wall Street career. But Seo soon turned Wall Street into his own private science lab, and his continued interest in deep questions mollified even his father. “Before he got into it, I strongly objected,” Tae Kun Seo says. “But now I think he’s not just grabbing money.” He has watched his son quit one firm to go to work for another, but never for a simple promotion; instead, John has moved to learn something new. Still, everywhere he goes, he has been drawn to a similar thorny problem: the right price to charge to insure against potential losses from extremely unlikely financial events. “Tail risk,” as it is known to quantitative traders, for where it falls in a bell-shaped probability curve. Tail risk, broadly speaking, is whatever financial cataclysm is believed by markets to have a 1 percent chance or less of happening. In the foreign-exchange market, the tail event might be the dollar falling by one-third in a year; in the bond market, it might be interest rates moving 3 percent in six months; in the stock market, it might be a 30 percent crash. “If there’s been a theme to John’s life,” says his brother Nelson, “it’s pricing tail.”
And if there has been a theme of modern Wall Street, it’s that young men with Ph.D.’s who approach money as science can cause more trouble than a hurricane. John Seo is oddly sympathetic to the complaint. He thinks that much of the academic literature about finance is nonsense, for instance. “These academics couldn’t understand the fact that they couldn’t beat the markets,” he says. “So they just said it was efficient. And, ‘Oh, by the way, here’s a ton of math you don’t understand.’ ” He notes that smart risk-takers with no gift for theory often end up with smart solutions to taking extreme financial risk — answers that often violate the academic theories. (“The markets are usually way ahead of the math.”) He prides himself on his ability to square book smarts with horse sense. As one of his former bosses puts it, “John was known as the man who could price anything, and his pricing felt right to people who didn’t understand his math.”
In the mid-1990s, when Wall Street first noticed money to be made covering the financial risks associated with hurricanes and earthquakes, it was inevitable that someone would call John Seo to ask him if he could figure out how to make sense of it. Until then, he had specialized in financial, not natural, disasters. But there was a connection between financial catastrophe and natural catastrophe. Both were extreme, both were improbable and both needed to be insured against. The firm that called him was Lehman Brothers, whose offer enticed Seo to quit his job and spend his first year at Lehman learning all he could about the old-fashioned insurance industry.
Right away, he could see the problem with natural catastrophe. An insurance company could function only if it was able to control its exposure to loss. Geico sells auto insurance to more than seven million Americans. No individual car accident can be foreseen, obviously, but the total number of accidents over a large population is amazingly predictable. The company knows from past experience what percentage of the drivers it insures will file claims and how much those claims will cost. The logic of catastrophe is very different: either no one is affected or vast numbers of people are. After an earthquake flattens Tokyo, a Japanese earthquake insurer is in deep trouble: millions of customers file claims. If there were a great number of rich cities scattered across the planet that might plausibly be destroyed by an earthquake, the insurer could spread its exposure to the losses by selling earthquake insurance to all of them. The losses it suffered in Tokyo would be offset by the gains it made from the cities not destroyed by an earthquake. But the financial risk from earthquakes — and hurricanes — is highly concentrated in a few places.
There were insurance problems that were beyond the insurance industry’s means. Yet insurers continued to cover them, sometimes unenthusiastically, sometimes recklessly. Why didn’t insurance companies see this? Seo wondered, and then found the answer: They hadn’t listened closely enough to Karen Clark.
Thirteen years before what would become Tropical Storm Katrina churned toward Florida — on Monday, Aug. 24, 1992 — Karen Clark walked from her Boston office to a nearby Au Bon Pain. Several hours earlier, Hurricane Andrew had struck Florida, and she knew immediately that the event could define her career. Back in 1985, while working for an insurance company, Clark wrote a paper with the unpromising title “A Formal Approach to Catastrophe Risk Assessment in Management.” In it, she made the simple point that insurance companies had no idea how much money they might lose in a single storm. For decades Americans had been lurching toward catastrophe. The 1970s and ’80s were unusually free of major storms. At the same time, Americans were cramming themselves and their wealth onto the beach. The insurance industry had been oblivious to the trends and continued to price catastrophic risk just as it always had, by the seat of its pants. The big insurance companies ran up and down the Gulf Coast selling as many policies as they could. No one — not even the supposed experts at Lloyd’s of London — had any idea of the scope of new development and the exposure that the insurance industry now had.
To better judge the potential cost of catastrophe, Clark gathered very long-term historical data on hurricanes. “There was all this data that wasn’t being used,” she says. “You could take it, and take all the science that also wasn’t being used, and you could package it in a model that could spit out numbers companies could use to make decisions. It just seemed like such an obvious thing to do.” She combined the long-term hurricane record with new data on property exposure — building-replacement costs by ZIP code, engineering reports, local building codes, etc. — and wound up with a crude but powerful tool, both for judging the probability of a catastrophe striking any one area and for predicting the losses it might inflict. Then she wrote her paper about it.
The attention Clark’s paper attracted was mostly polite. Two years later, she visited Lloyd’s — pregnant with her first child, hauling a Stone Age laptop — and gave a speech to actual risk-takers. In nature’s casino, they had set themselves up as the house, and yet they didn’t know the odds. They assumed that even the worst catastrophe could generate no more than a few billion dollars in losses, but her model was generating insured losses of more than $30 billion for a single storm — and these losses were far more likely to occur than they had been in the previous few decades. She projected catastrophic storms from the distant past onto the present-day population and storms from the more recent past onto richer and more populated areas than they had actually hit. (If you reran today the hurricane that struck Miami in 1926, for instance, it would take out not the few hundred million dollars of property it destroyed at the time but $60 billion to $100 billion.) “But,” she says, “from their point of view, all of this was just in this computer.”
She spoke for 45 minutes but had no sense that she had been heard. “The room was very quiet,” she says. “No one got up and left. But no one asked questions either. People thought they had already figured it out. They were comfortable with their own subjective judgment.” Of course they were; they had made pots of money the past 20 years insuring against catastrophic storms. But — and this was her real point — there hadn’t been any catastrophic storms! The insurers hadn’t been smart. They had been lucky.
Clark soon found herself in a role for which she was, on the surface at least, ill suited: fanatic. “I became obsessed with it,” she says. One big player in the insurance industry took closer notice of her work and paid her enough to start a business. Applied Insurance Research, she called it, or A.I.R. Clark hired a few scientists and engineers, and she set to work acquiring more and better data and building better models. But what she really was doing — without quite realizing it — was waiting, waiting for a storm.
Hurricane Andrew made landfall at 5 on a Monday morning. By 9 she knew only the path of the storm and its intensity, but the information enabled her to estimate the losses: $13 billion, give or take. If builders in South Florida had ignored the building codes and built houses to lower standards, the losses might come in even higher. She faxed the numbers to insurers, then walked to Au Bon Pain. Everything was suddenly more vivid and memorable. She ordered a smoked-turkey and Boursin cheese sandwich on French bread, with lettuce and tomato, and a large Diet Coke. It was a nice sunny day in Boston. She sat outside at a small black table, alone. “It was too stressful to be with other people,” she says. “I didn’t want to even risk a conversation.” She ate in what she describes as “a catatonic state.” The scuttlebutt from Lloyd’s already had it that losses couldn’t possibly exceed $6 billion, and some thought they were looking at a loss of just a few hundred million. “No one believed it,” she says of her estimate. “No one thought it was right. No one said, ‘Yeah, $13 billion sounds like a reasonable number.’ ” As she ate, she wondered what $13 billion in losses looked like.
When she returned to the office, her phones were ringing. “People were outraged,” she says. “They thought I was crazy.” One insurance guy called her, chortling. “A few mobile homes and an Air Force base — how much could it be?” he said.
It took months for the insurers to tote up their losses: $15.5 billion. (Building codes in South Florida had not been strictly enforced.) Fifteen and a half billion dollars exceeded all of the insurance premiums ever collected in Dade County. Eleven insurance companies went bust. And this wasn’t anything like the perfect storm. If it had gone into Miami, it could have bankrupted the whole industry. Clark had been right: the potential financial losses from various catastrophes were too great, and too complicated, to be judged by human intuition. “No one ever called to congratulate me,” Clark says, laughing. “But I had a lot of people call and ask to buy the model.”
After Hurricane Andrew came a shift in the culture of catastrophe. “This one woman really created the method for valuing this risk,” says John Seo. Clark’s firm, A.I.R., soon had more than 25 Ph.D.’s on staff and two competitors, Eqecat and Risk Management Solutions. In its Bay Area offices, R.M.S. now houses more than 100 meteorologists, seismologists, oceanographers, physicists, engineers and statisticians, and they didn’t stop at hurricanes and earthquakes but moved on to flash floods, wildfires, extreme winter storms, tornadoes, tsunamis and an unpleasant phenomenon delicately known as “extreme mortality,” which, more roughly speaking, is the possibility that huge numbers of insured human beings will be killed off by something like a global pandemic.
The models these companies created differed from peril to peril, but they all had one thing in common: they accepted that the past was an imperfect guide to the future. No hurricane has hit the coast of Georgia, for instance, since detailed records have been kept. And so if you relied solely on the past, you would predict that no hurricane ever will hit the Georgia coast. But that makes no sense: the coastline above, in South Carolina, and below, in Florida, has been ravaged by storms. “You are dealing with a physical process,” says Robert Muir-Wood, the chief scientist for R.M.S. “There is no physical reason why Georgia has not been hit. Georgia’s just been lucky.” To evaluate the threat to a Georgia beach house, you need to see through Georgia’s luck. To do this, the R.M.S. modeler creates a history that never happened: he uses what he knows about actual hurricanes, plus what he knows about the forces that create and fuel hurricanes, to invent a 100,000-year history of hurricanes. Real history serves as a guide — it enables him to see, for instance, that the odds of big hurricanes making landfall north of Cape Hatteras are far below the odds of them striking south of Cape Hatteras. It allows him to assign different odds to different stretches of coastline without making the random distinctions that actual hurricanes have made in the last 100 years. Generate a few hundred thousand hurricanes, and you generate not only dozens of massive hurricanes that hit Georgia but also a few that hit, say, Rhode Island.
The companies’ models disagreed here and there, but on one point they spoke with a single voice: four natural perils had outgrown the insurers’ ability to insure them — U.S. hurricane, California earthquake, European winter storm and Japanese earthquake. The insurance industry was prepared to lose $30 billion in a single event, once every 10 years. The models showed that a sole hurricane in Florida wouldn’t have to work too hard to create $100 billion in losses. There were concentrations of wealth in the world that defied the logic of insurance. And most of them were in America.
The more John Seo looked into the insurance industry, the more it seemed to be teetering at the edge of ruin. This had happened once before, in 1842, when the city of Hamburg burned to the ground and bankrupted the entire German insurance industry many times over. Out of the ashes was born a new industry, called reinsurance. The point of reinsurance was to take on the risk that the insurance industry couldn’t dilute through diversification — say, the risk of an entire city burning to the ground or being wiped off the map by a storm. The old insurance companies would still sell policies to the individual residents of Hamburg. But they would turn around and hand some of the premiums they collected to Cologne Re (short for reinsurance) in exchange for taking on losses over a certain amount. Cologne Re would protect itself by diversifying at a higher level — by selling catastrophic fire insurance to lots of other towns.
But by their very nature, the big catastrophic risks of the early 21st century couldn’t be diversified away. Wealth had become far too concentrated in a handful of extraordinarily treacherous places. The only way to handle them was to spread them widely, and the only way to do that was to get them out of the insurance industry and onto Wall Street. Today, the global stock markets are estimated at $59 trillion. A 1 percent drop in the markets — not an unusual event — causes $590 billion in losses. The losses caused by even the biggest natural disaster would be a drop in the bucket to the broader capital markets. “If you could take a Magnitude 8 earthquake and distribute its shock across the planet, no one would feel it,” Seo says. “The same principle applies here.” That’s where catastrophe bonds came in: they were the ideal mechanism for dissipating the potential losses to State Farm, Allstate and the other insurers by extending them to the broader markets.
Karen Clark’s model was, for Seo, the starting point. When he first stumbled upon it and the other companies’ models, he found them “guilty until proven innocent,” as he puts it. “I could see the uncertainty in them,” he says, “just by looking at the different numbers they generated for the same storm.” When they run numbers to see what would happen if the 1926 Miami hurricane hit the city today, A.I.R. puts the losses at $80 billion, R.M.S. at $106 billion and Eqecat at $63 billion. They can’t all be right. But they didn’t need to be exactly right, just sort of right, and the more he poked around inside them, the more he felt they were better than good enough to underpin financial decisions. They enabled you to get a handle on the risk as best you could while acknowledging that you would never know it exactly. And after all, how accurate were the models that forecast the likelihood that Enron would collapse? Next to what Wall Street investors tried to predict every day, natural disasters seemed almost stable. “In the financial markets, you have to care what other people think, even if what they think is screwed up,” Seo says. “Crowd dynamics build on each other. But these things — hurricanes, earthquakes — don’t exhibit crowd behavior. There’s a real underlying risk you have to understand. You have to be a value investor.”
The models were necessary but insufficient. True, they gave you a rough sense of the expected financial losses, but they said nothing about the rewards. Financial markets exist only as long as investors feel the odds are stacked in their favor. Investors — unlike roulette players — can honestly expect to make a gain (their share in the profits of productive enterprise). But how big a gain? How should the payout vary, from government bonds to blue-chip stocks to subprime mortgages? The rewards in each market tended to vary with investors’ moods, but those in catastrophe insurance were just incredibly volatile. Hurricane insurance rates would skyrocket after a big storm, then settle back down. This wouldn’t do: if big investors were going to be persuaded to take billions of dollars in catastrophic risk, they would need to feel there was some reason in the pricing of that risk. “The market,” as Seo puts it, “needs an acceptable mode of failure.”
In the spring of 2001, to the surprise of his colleagues, Seo left his big Wall Street firm and opened a hedge fund — which, he announced, wouldn’t charge its investors the standard 2 percent of assets and 20 percent of returns but a lower, flat fee. “It was quixotic,” says Paul Puleo, a former executive at Lehman who worked with Seo. “He quits this high-paying job to basically open a business in his garage in a market that doesn’t exist.” Seo opened his new shop with his younger brother Nelson and then brought in their older brother, Michael. (His third brother, Scott, had studied astrophysics but decided that “there was no future in astrophysics” and eventually turned himself into an ophthalmologist.) Seo named his firm Fermat Capital Management, after one of his intellectual heroes. “I had once read the letters between Pierre de Fermat and Blaise Pascal,” he wrote in a recent e-mail message. “From my father I had learned that most great mathematicians were nasty guys and total jerks (check out Isaac Newton . . . extra nasty guy), but when I read the Fermat-Pascal letters, you could see that Fermat was an exception to the stereotype . . . truly a noble person. I loved his character and found that his way of analyzing profitless games of chance (probability theory) was the key to understanding how to analyze profitable games of chance (investment theory).”
Four years later, Seo’s hedge fund still faced two problems. The smaller one was that investors were occasionally slow to see the appeal of an investment whose first name was catastrophe. As one investor put it, “My boss won’t let me buy bonds that I have to watch the Weather Channel to follow.” That objection doesn’t worry Seo much. “Investors who object to cat-bond investing usually say that it’s just gambling,” he says. “But the more mature guys say: ‘That’s what investing is. But it’s gambling with the odds in your favor.’ ”
His bigger problem was that insurance companies still didn’t fully understand their predicament: they had $500 billion in exposure to catastrophe but had sold only about $5 billion of cat bonds — a fifth of them to him. Still, he could see their unease in their prices: hurricane- and earthquake-insurance premiums bounced around madly from year to year. Right after Andrew, the entire industry quintupled its prices; a few tranquil years later, prices were back down nearly to where they had been before the storm. Financial markets bounced around wildly too, of course, but in the financial markets, the underlying risks (corporate earnings, people’s moods) were volatile. The risk in natural-disaster insurance was real, physical and, in principle, quantifiable, and from year to year it did not change much, if at all. In effect, the insurers weren’t insuring against disaster; they were only pretending to take the risk, without actually doing so, and billing their customers retroactively for whatever losses they incurred. At the same time, they were quietly sneaking away from catastrophe. Before the 1994 Northridge earthquake, more than a third of California homeowners had quake insurance; right after, the insurers fled the market, so that fewer than 15 percent of California homeowners have earthquakes in their policies today.
The market was broken: people on fault lines and beachfronts were stuck either paying far too much for their insurance or with no real coverage except the vague and corrupting hope that, in a crisis, the government would bail them out. A potentially huge, socially beneficial market was moments from birth. All it needed was a push from nature. And so on Aug. 24, 2005, John Seo was waiting, waiting for a storm. And here it came.
Wall Street is a machine for turning information nobody cares about into information people can get rich from. Back when banks lent people money to buy homes and then sat around waiting for interest payments, no one thought to explore how quickly homeowners would refinance their mortgages if interest rates fell. But then Wall Street created a market in mortgage bonds, and the trader with better information about how and when people refinance made a killing. There’s now a giant subindustry to analyze the inner financial life of the American homeowner.
Catastrophe bonds do something even odder: they financialize storms. Once there’s a market for cat bonds, there’s money to be made, even as a storm strikes, in marginally better weathermen. For instance, before the 2005 hurricane season, a Bermuda cat-bond hedge fund called Nephila found a team of oceanographers in Rhode Island called Accurate Environmental Forecasting, whose forecasts of hurricane seasons had been surprisingly good. Nephila rented the company’s services and traded bonds on the back of its reports. “They kind of chuckle at what we do,” says a Nephila founder, Frank Majors. “The fact that we’re making $10 million bets on whether Charley is going to hit Tampa or not. It made them a little nervous at first. We told them not to worry about what we’re going to do with the information. Just give it to us.”
As Katrina bore down on New Orleans, a cat bond named Kamp Re, issued by the insurance company Zurich, was suddenly at risk. If Zurich lost more than $1.2 billion on a single hurricane in about a two-year period, investors would lose all their money. If Zurich represented about 3 percent of the U.S. insurance market — that is, it was on the hook for about 3 percent of the losses — a hurricane would need to inflict about $40 billion in damage to trigger the default. Since no event as big as this had ever happened, it was hard to say just how likely it was to happen. According to R.M.S., there was a 1.08 percent chance that Kamp Re bond holders would lose all their money — assuming the scientists really understood the odds. The deal had been a success. One of its biggest buyers was John Seo.
As Katrina spun, the players in nature’s casino gathered around the table. When the storm jogged east and struck not New Orleans directly but the less populated, and less wealthy, coastline between Louisiana and Mississippi, they all had the same reaction — relief — but Hemant Shah felt a special relief. Shah is one of the founders of R.M.S., and he was at that moment driving to catch a flight from San Francisco to New York, where he hoped to speak at a conference devoted to predicting terrorism. When he saw Katrina miss New Orleans, he said to himself, O.K., it’s big, but it’s not catastrophic, and he boarded his plane.
As he flew across the country, R.M.S. and its competitors replicated Katrina inside their computers in much the same way that Karen Clark had once replicated Hurricane Andrew. Just hours after landfall, all three firms sent clients in the insurance industry their best estimates of financial losses: R.M.S. put them at $10 billion to $25 billion; Eqecat called for a range between $9 billion and $16 billion; Clark’s A.I.R. had a range of $12.7 billion to $26.5 billion. Big, as Shah said, but not catastrophic. Traders who had underwritten Kamp Re took calls from an investor at a Japanese bank in London. Cheered by Katrina’s path, the fellow was looking to buy some Kamp Re bonds. The traders found another investor eager to unload his Kamp Re holdings. The London investor bought $10 million of Kamp Re at a price of $94.
John Seo just watched. For the past four years, he and his brothers had made money at such moments as this: “live” cat trading, it’s called. A few investors would inevitably become jittery and sell their cat bonds at big discounts, what with the Weather Channel all hysteria all the time. (“The worst place to go if you’re taking risks,” says one cat-bond investor, “is the Weather Channel. They’re just screaming all the time.”) But entering the 2005 hurricane season, the Seo brothers had reconsidered their habit of buying in a storm. “The word had gotten out that buying in the storm was the smart thing to do,” Seo says. “And we wer