Description
Read the article “In Nature’s Casino” attached and answer the following questions:
Read the article “In Nature’s Casino” by Michael Lewis. Discuss the following points and how they relate to the examples in the article. Be specific and quote from the article in answering each of the questions and whenever possible.
1. In Michael Lewis’s terms, what is a CAT bond? (5 points)
2. Why is the principle of risk pooling and diversification important to the insurance and reinsurance industry, and how does the capital market contribute to this? (5 points)
3. What are some examples where heuristic thinking falls short in understanding catastrophe risk? (5 points)
4. What are your thoughts on the future of financing “tail risk” (e.g., natural catastrophe, climate risk, financial disaster, pandemic, war, etc.) (5 points)
Watch and Listen to the “regulating insurer use of genetic information” YouTube video by Anya Prince (linked here) and answer the following questions:
A) The following questions are related to the presentation by Anya Prince on “Regulating Insurer Use of Genetic Information”.
1. What is adverse selection in an insurance market, and according to adverse selection theory, what are the predictions when life insurers are prohibited from using genetic information in underwriting?
2. According to the presentation, how do insurers’ perspectives and societal views on ‘unfair discrimination’ differ?
3. Think of a question you’d like to ask Anya Prince after her presentation. Your question should not be identical to the ones asked by the audience at the end of the video.
Please Answer the following random open response question:
1) A new pilot project was launched to support low- and moderate-income (LMI) communities in high-flood-risk neighborhoods in NYC with emergency cash funds after a major flood. The program will provide parametric coverage for excess rainfall and storm surge events that can lead to severe flooding. The Center for NYC Neighborhoods (CNYCN) receives the proceeds of a parametric payout, which supports their emergency cash grants to eligible New Yorkers.
List one potential benefit and one potential drawback of this innovative insurance product. (10 points.)
2) NeedOil is a firm that uses oil in its production process. If the price of oil increases, then the firm’s cost will increase. We assume that NeedOil cannot pass on all incremental costs to customers and therefore that cost increase will result in lower profits. Suppose that NeedOil does not want to bear the risk that it will have to pay more than $120 a barrel for oil next year, while it does not believe oil prices will rise above $150 a barrel.
What option trading strategy would you recommend?
Unformatted Attachment Preview
https://www.nytimes.com/2007/08/26/magazine/26neworleanst.html
In Nature’s Casino
By Michael Lewis
Aug. 26, 2007
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 preexisting 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 — buildingreplacement 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.’ ”
Courting destruction in Palm Beach,
Fla.
Sasha Bezzubov for The New York Times
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 were afraid our past
successes would give us an irrational interest in buying. Everything’s all fuzzy in
these events. And when things are fuzzy, your brain gives you an excuse to push the
envelope. So we adopted a policy, before the season, of staying out of the market.”
A few hours later, Hemant Shah’s plane landed in New York. Shah turned on his
BlackBerry and discovered that the New Orleans levees had broken: much of the
city would soon be underwater. “My first reaction,” Shah says, “was, Uh-oh, we have
a problem.” In the imaginary 100,000-year history of hurricanes that R.M.S. had in
its computers, no hypothetical storm that struck so far from New Orleans had ever
caused the levees to fail. The models, like the intuition they replaced, had a blind
spot.
The Kamp Re bonds collapsed, the price dropping from the mid-90s to the low 20s. A
few weeks later, an announcement from Zurich American made it clear that the
investors in Kamp Re wouldn’t be getting any money back, and Kamp Re’s price fell
from $20 to 10 cents. But then the real trouble started: R.M.S., the modeling
company, declared that it was rethinking the whole subject of hurricane risk. Since
1995, scientists had noted a distinct uptick in hurricane activity in the North Atlantic
Basin. The uptick had been ignorable because the storms had not been making
landfall. But between July 2004 and the end of 2005, seven of history’s most
expensive hurricanes had struck the American coast, leaving behind 5.5 million
insurance claims and $81 billion in insured losses. The rise in hurricane size and
frequency was no longer ignorable. R.M.S. convened a panel of scientists. The
scientists agreed that unusually warm sea-surface temperatures were causing
