1 00:00:02,800 --> 00:00:07,160 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. 2 00:00:08,160 --> 00:00:11,400 Speaker 2: The most profitable hedge fund strategy of last year was 3 00:00:11,440 --> 00:00:15,160 Speaker 2: buying something a lot of people don't like thinking about 4 00:00:15,520 --> 00:00:22,760 Speaker 2: cat bonds. They have, unfortunately nothing to do with cats. 5 00:00:23,360 --> 00:00:27,080 Speaker 2: In this case, cat is short for catastrophe, and cat 6 00:00:27,120 --> 00:00:29,800 Speaker 2: bonds are for extreme weather events. 7 00:00:30,520 --> 00:00:35,800 Speaker 1: Cat bonds are only four very rare, very catastrophic events, 8 00:00:36,200 --> 00:00:40,440 Speaker 1: whether it's California quakes or even a Japanese tsunami. 9 00:00:40,760 --> 00:00:43,760 Speaker 2: We talked about them with Gautam Nike, a Bloomberg reporter 10 00:00:43,880 --> 00:00:45,440 Speaker 2: and editor based in London. 11 00:00:45,720 --> 00:00:49,800 Speaker 1: The way it works is that if a defined event 12 00:00:50,440 --> 00:00:54,639 Speaker 1: doesn't occur, then the investor they get to keep their 13 00:00:54,680 --> 00:00:58,640 Speaker 1: money and they get paid a very handsome risk premium. 14 00:00:59,320 --> 00:01:03,640 Speaker 1: If that catastrophe as defined does occur, they can lose 15 00:01:03,680 --> 00:01:06,080 Speaker 1: some of their money or all of the money. So 16 00:01:06,120 --> 00:01:09,959 Speaker 1: it's a big risk and it's a gamble against mother Nature. 17 00:01:10,640 --> 00:01:13,319 Speaker 2: Gautum told us that while these bonds have been around 18 00:01:13,360 --> 00:01:16,520 Speaker 2: since the nineteen nineties, they've been growing in popularity recently. 19 00:01:16,959 --> 00:01:20,240 Speaker 2: A record sixteen billion dollars in cat bonds were issued 20 00:01:20,240 --> 00:01:23,520 Speaker 2: in twenty twenty three, bringing the total market value to 21 00:01:23,680 --> 00:01:27,880 Speaker 2: forty five billion today. On the show how cat bonds 22 00:01:27,920 --> 00:01:30,319 Speaker 2: went from a niche corner of the financial world to 23 00:01:30,400 --> 00:01:33,760 Speaker 2: becoming one of the market's hottest items, and how the 24 00:01:33,840 --> 00:01:38,280 Speaker 2: increasing risks from climate change might complicate that. This is 25 00:01:38,480 --> 00:01:47,960 Speaker 2: the big take from Bloomberg News. I'm Sarah Holder, So Gadam, 26 00:01:48,520 --> 00:01:51,160 Speaker 2: how do catastrophe bonds work? 27 00:01:51,880 --> 00:01:56,160 Speaker 1: So what catastrophe bond is a specialized form of insurance. 28 00:01:56,840 --> 00:02:01,960 Speaker 1: So typically an insurance company would issue a cat bond 29 00:02:02,480 --> 00:02:04,480 Speaker 1: if it didn't want to take on the risk of 30 00:02:04,600 --> 00:02:09,440 Speaker 1: a Katrina like event occurring and destroying tens of thousands 31 00:02:09,520 --> 00:02:12,440 Speaker 1: of homes and leaving the insurer on the hook for 32 00:02:12,480 --> 00:02:14,920 Speaker 1: a huge amount of money. So cat bond is a 33 00:02:14,919 --> 00:02:17,519 Speaker 1: way to pass on this risk to Wall Street, which 34 00:02:17,520 --> 00:02:20,240 Speaker 1: is not traditionally part of the insurance industry. 35 00:02:20,639 --> 00:02:24,200 Speaker 2: Catastrophe bonds are usually held for three to five years, 36 00:02:24,720 --> 00:02:27,320 Speaker 2: and Gatham says the length is set that way for 37 00:02:27,360 --> 00:02:27,840 Speaker 2: a reason. 38 00:02:28,080 --> 00:02:31,320 Speaker 1: So it's not for ten, fifteen, twenty years that would 39 00:02:31,360 --> 00:02:35,280 Speaker 1: be a huge risk. It's for a very narrow specific timeframe. 40 00:02:36,120 --> 00:02:40,240 Speaker 2: But even in that specific timeframe, weather is notoriously hard 41 00:02:40,280 --> 00:02:43,560 Speaker 2: to predict. Have you ever used a weather app So 42 00:02:43,800 --> 00:02:47,000 Speaker 2: Gautam wanted to understand how the people buying these bonds 43 00:02:47,040 --> 00:02:50,400 Speaker 2: were deciding which disasters to bet against. And when it 44 00:02:50,440 --> 00:02:53,680 Speaker 2: comes to cat bonds, there's no firm that's bought more 45 00:02:53,720 --> 00:02:58,280 Speaker 2: of them than for Matt's Capital Management. So Gadam went 46 00:02:58,320 --> 00:03:00,200 Speaker 2: to Connecticut to visit them. 47 00:03:00,760 --> 00:03:03,239 Speaker 1: So, you know, a typical hedge fund might have I 48 00:03:03,240 --> 00:03:06,880 Speaker 1: don't know, marble floors and expensive art hanging in the 49 00:03:08,560 --> 00:03:10,760 Speaker 1: on the walls, and you know, might be located in 50 00:03:10,800 --> 00:03:15,480 Speaker 1: central Manhattan. But Fermat is quite different. You know, it's 51 00:03:15,520 --> 00:03:19,360 Speaker 1: based in a pretty affluent town of Westport and Connecticut, 52 00:03:19,760 --> 00:03:22,960 Speaker 1: but it's on a fairly modest street and opposite a 53 00:03:23,040 --> 00:03:28,639 Speaker 1: car repair shop. If you enter the offices of Fermat, 54 00:03:29,320 --> 00:03:33,959 Speaker 1: you know, you basically find meteorology and weather journals, a 55 00:03:33,960 --> 00:03:37,320 Speaker 1: lot of wonky literature. In the reception area, you have 56 00:03:37,360 --> 00:03:40,920 Speaker 1: a lot of white boards where equations and physics details 57 00:03:40,920 --> 00:03:41,680 Speaker 1: are scrawled. 58 00:03:41,920 --> 00:03:45,440 Speaker 2: At the center of all these whiteboards is John so For, 59 00:03:45,560 --> 00:03:47,680 Speaker 2: Matt's co founder and managing director. 60 00:03:47,920 --> 00:03:51,839 Speaker 1: He has a background in biophysics, and I think he's 61 00:03:51,960 --> 00:03:56,400 Speaker 1: really used that scientific and mathematical acumen to good stead 62 00:03:57,120 --> 00:04:01,040 Speaker 1: and sort of created his own models and his own 63 00:04:01,200 --> 00:04:02,760 Speaker 1: mathematical techniques. 64 00:04:03,080 --> 00:04:07,040 Speaker 2: One of Soo's earliest finance jobs involved creating unique derivatives 65 00:04:07,480 --> 00:04:09,640 Speaker 2: to cover seemingly random events. 66 00:04:10,000 --> 00:04:14,400 Speaker 1: So, for example, you know a charity that was organizing 67 00:04:14,520 --> 00:04:18,280 Speaker 1: a golf tournament and would give somebody a car. If 68 00:04:18,320 --> 00:04:21,720 Speaker 1: someone hit a hole in one, what is the likelihood 69 00:04:22,000 --> 00:04:25,599 Speaker 1: of that hole in one occurring? And if it does occur, 70 00:04:25,720 --> 00:04:28,000 Speaker 1: the charity might be wiped out. They'd be on the 71 00:04:28,040 --> 00:04:31,800 Speaker 1: hook for you know, losing the car or prize money. 72 00:04:32,200 --> 00:04:34,960 Speaker 1: So you would want to buy insurance against that happening. 73 00:04:35,040 --> 00:04:40,800 Speaker 1: So he would calculate that kind of risk and create 74 00:04:40,839 --> 00:04:44,400 Speaker 1: a derivative product that would you know, protect the charity 75 00:04:44,440 --> 00:04:46,240 Speaker 1: and get someone did hit that hole in one. 76 00:04:46,600 --> 00:04:48,880 Speaker 2: So told Gautam that when he started from matt with 77 00:04:48,920 --> 00:04:51,320 Speaker 2: his brother in two thousand and one, he set out 78 00:04:51,360 --> 00:04:53,760 Speaker 2: to build a science based model that would weigh the 79 00:04:53,800 --> 00:04:56,920 Speaker 2: probability of a natural disaster against the returns on a 80 00:04:56,920 --> 00:05:00,040 Speaker 2: cat bond. And his method starts with gathering a a 81 00:05:00,080 --> 00:05:01,880 Speaker 2: bunch of meteorological data. 82 00:05:01,960 --> 00:05:06,359 Speaker 1: There are specialized companies that take a lot of the 83 00:05:06,400 --> 00:05:10,000 Speaker 1: meteorological data and crunch it and they come up with 84 00:05:10,279 --> 00:05:14,840 Speaker 1: sort of a magic number what is the estimated loss 85 00:05:15,040 --> 00:05:18,680 Speaker 1: of a particular event. So let's say a California earthquake. 86 00:05:19,400 --> 00:05:23,320 Speaker 1: What is the likelihood that a particular earthquake could occur 87 00:05:23,400 --> 00:05:26,280 Speaker 1: in a particular year in a particular area of California 88 00:05:26,279 --> 00:05:28,920 Speaker 1: where there are a lot of expensive homes. Well, you'd 89 00:05:28,920 --> 00:05:32,240 Speaker 1: have to know your plate tectonics, you have to understand 90 00:05:32,279 --> 00:05:35,040 Speaker 1: the history of earthquakes. You'll have to look at the 91 00:05:35,120 --> 00:05:38,920 Speaker 1: data that shows who has built homes in which area. 92 00:05:39,600 --> 00:05:43,080 Speaker 1: And there are specialized companies that provide this kind of information. 93 00:05:43,200 --> 00:05:48,360 Speaker 1: So what John so does is he buys these models 94 00:05:48,360 --> 00:05:52,720 Speaker 1: from these companies, but then he does his own calculation. 95 00:05:52,800 --> 00:05:55,080 Speaker 1: On top of that, he adds his own magic source 96 00:05:55,520 --> 00:05:58,760 Speaker 1: to refine it so that he can sort of beat 97 00:05:58,800 --> 00:06:02,320 Speaker 1: the capond market and perhaps beat other investors. 98 00:06:02,880 --> 00:06:07,600 Speaker 2: And this magic sauce includes more unconventional methods like watching 99 00:06:07,720 --> 00:06:11,440 Speaker 2: hours of film of California wildfires to understand where they're 100 00:06:11,480 --> 00:06:15,480 Speaker 2: happening and how they're spreading, anything that might help refine 101 00:06:15,600 --> 00:06:19,120 Speaker 2: the data coming in. And that also includes a model 102 00:06:19,240 --> 00:06:22,280 Speaker 2: that has made for Matt's betting approach unique, one that 103 00:06:22,360 --> 00:06:26,120 Speaker 2: borrows from out of all things airplane physics. 104 00:06:26,520 --> 00:06:32,440 Speaker 1: He plotted one curve, which was the probability of a 105 00:06:32,760 --> 00:06:38,240 Speaker 1: catastrophe against the potential losses. And then he plotted another 106 00:06:38,320 --> 00:06:42,080 Speaker 1: line which showed how much profit he could make at 107 00:06:42,160 --> 00:06:45,840 Speaker 1: different levels of risk. And when he put those two 108 00:06:45,880 --> 00:06:49,880 Speaker 1: together on a piece of paper, they looked like the 109 00:06:50,040 --> 00:06:54,080 Speaker 1: cross section of a wing. Once he realized, WHOA, this 110 00:06:54,240 --> 00:06:57,400 Speaker 1: is just like a wing of an airplane. I can 111 00:06:57,440 --> 00:07:01,560 Speaker 1: look at the physics of lift to compute the risk 112 00:07:01,680 --> 00:07:05,280 Speaker 1: and reward at every point on those curves, and once 113 00:07:05,320 --> 00:07:08,400 Speaker 1: you calculate that, you have a slightly better idea, quite 114 00:07:08,400 --> 00:07:11,800 Speaker 1: a better idea of whether to invest in a particular 115 00:07:12,040 --> 00:07:13,120 Speaker 1: set of bonds or not. 116 00:07:14,320 --> 00:07:17,160 Speaker 2: But like other cat bond investors, a key part of 117 00:07:17,240 --> 00:07:21,520 Speaker 2: Sow's strategy is to diversify. For Matt's bonds cover properties 118 00:07:21,560 --> 00:07:23,160 Speaker 2: from Florida to New Zealand. 119 00:07:23,440 --> 00:07:26,040 Speaker 1: So typical hedge fund would buy one hundred or two 120 00:07:26,160 --> 00:07:29,040 Speaker 1: hundred different cat bonds. One would be save for a 121 00:07:29,040 --> 00:07:32,360 Speaker 1: typhoon in the Philippines. What would be against a tsunami 122 00:07:32,360 --> 00:07:37,400 Speaker 1: occurring in Japan. One would be against a wildfire in California. Now, 123 00:07:37,440 --> 00:07:43,600 Speaker 1: the likelihood statistical likelihood of several of those natural disasters 124 00:07:44,120 --> 00:07:47,080 Speaker 1: which are down to Mother Nature, really they're pure luck, right, 125 00:07:48,040 --> 00:07:52,040 Speaker 1: The chances of those occurring in the same year are 126 00:07:52,160 --> 00:07:54,800 Speaker 1: very very low. So even if one or two or 127 00:07:54,840 --> 00:08:00,000 Speaker 1: three get triggered, these investors have positions in a whole 128 00:08:00,080 --> 00:08:02,560 Speaker 1: bunch of other cat owns that don't get treated, so 129 00:08:02,600 --> 00:08:04,160 Speaker 1: they don't lose money on those. 130 00:08:04,680 --> 00:08:08,240 Speaker 2: For Matt So has taken catbond trading to the next level. 131 00:08:08,800 --> 00:08:11,480 Speaker 2: The company accounts for a quarter of the entire cat 132 00:08:11,560 --> 00:08:14,880 Speaker 2: bond market and had returns around twenty percent in twenty 133 00:08:14,920 --> 00:08:18,960 Speaker 2: twenty three. That's way higher than the average eight percent 134 00:08:19,000 --> 00:08:21,560 Speaker 2: returns brought in by the rest of the hedge fund market, 135 00:08:22,000 --> 00:08:25,880 Speaker 2: and So is really confident in his calculations here he 136 00:08:26,000 --> 00:08:27,000 Speaker 2: is on Bloomberg TV. 137 00:08:27,600 --> 00:08:30,800 Speaker 1: Is the market or cat bron prices a better predictor 138 00:08:31,240 --> 00:08:33,959 Speaker 1: of where hurricanes might make landfall or perhaps the kind 139 00:08:34,000 --> 00:08:37,680 Speaker 1: of damage they may cause? Then an outfit like the NAA, Right, 140 00:08:37,760 --> 00:08:40,040 Speaker 1: I think that there as good a predictor as you're 141 00:08:40,080 --> 00:08:40,920 Speaker 1: going to get. 142 00:08:41,920 --> 00:08:45,160 Speaker 2: After the break. What happens when the disasters cat bond 143 00:08:45,200 --> 00:08:56,800 Speaker 2: traders are betting against get more unpredictable. Hey, we're back. 144 00:08:57,480 --> 00:09:00,400 Speaker 2: Before the break. We were talking with Gautum Nike about 145 00:09:00,440 --> 00:09:03,200 Speaker 2: catastrophe bonds and the man at the center of the 146 00:09:03,200 --> 00:09:07,000 Speaker 2: firm that owns the most of them, John So. With 147 00:09:07,040 --> 00:09:10,440 Speaker 2: the weather getting more unpredictable and extreme, I wanted to 148 00:09:10,480 --> 00:09:14,320 Speaker 2: know what does that mean for So's models. Gadam says, 149 00:09:14,440 --> 00:09:17,080 Speaker 2: So's calculations are some of the best in the industry, 150 00:09:17,360 --> 00:09:18,840 Speaker 2: but they aren't bulletproof. 151 00:09:19,200 --> 00:09:23,480 Speaker 1: You know, when Hurricane Katrina hit, his portfolio lost three percent, 152 00:09:24,000 --> 00:09:26,520 Speaker 1: So he has his down years. You know, if there's 153 00:09:26,520 --> 00:09:29,480 Speaker 1: a big catastrophe and he's holding cap bonds linked to that, 154 00:09:29,960 --> 00:09:32,520 Speaker 1: he will suffer a hit just like any other investor. 155 00:09:32,840 --> 00:09:35,640 Speaker 2: And Gautam says the way some cat bonds are structured 156 00:09:35,760 --> 00:09:38,240 Speaker 2: also means that investors can be on the hook for 157 00:09:38,400 --> 00:09:42,680 Speaker 2: less rare weather events what are called secondary perils. 158 00:09:43,040 --> 00:09:51,760 Speaker 1: You have a lot more mid size natural disasters, so flooding, wildfires, tornadoes, 159 00:09:51,960 --> 00:09:56,880 Speaker 1: and thunderstorms. Very often an insurance company will take a 160 00:09:56,960 --> 00:10:00,640 Speaker 1: hurricane and package it with secondary I'll say, here's a 161 00:10:00,679 --> 00:10:04,520 Speaker 1: cat bond. It covers a hurricane, but also covers floods, 162 00:10:04,559 --> 00:10:08,360 Speaker 1: and it also covers wildfires. So as an investor, if 163 00:10:08,360 --> 00:10:12,160 Speaker 1: you buy this cat bond, you know about the earthquake stuff, 164 00:10:12,200 --> 00:10:15,000 Speaker 1: you're quite confident you have got to handle on the 165 00:10:15,080 --> 00:10:18,080 Speaker 1: risk there. But the stuff on floods, the stuff on 166 00:10:18,160 --> 00:10:22,320 Speaker 1: wildfire you have much less certainty about. So when you 167 00:10:22,360 --> 00:10:24,640 Speaker 1: take on that cat bond, when you buy it, you're 168 00:10:24,679 --> 00:10:26,880 Speaker 1: taking on risk you don't fully understand. 169 00:10:27,080 --> 00:10:30,800 Speaker 2: So instead of predicting Hurricane Katrina, so it could be 170 00:10:30,840 --> 00:10:33,760 Speaker 2: predicting a thunderstorm in New York City. 171 00:10:34,000 --> 00:10:35,080 Speaker 1: That's right exactly. 172 00:10:35,559 --> 00:10:39,120 Speaker 2: So my last question, katim if so has a better 173 00:10:39,200 --> 00:10:42,400 Speaker 2: model to predict these natural disasters or the risk levels 174 00:10:42,440 --> 00:10:45,080 Speaker 2: of these natural disasters using a lot of data and 175 00:10:45,120 --> 00:10:48,640 Speaker 2: a lot of know how and technical expertise. This seems 176 00:10:48,679 --> 00:10:51,680 Speaker 2: like information that governments and cities might want and that 177 00:10:51,760 --> 00:10:55,400 Speaker 2: residents might want. It could save lives, help people anticipate 178 00:10:55,480 --> 00:11:00,199 Speaker 2: devastating losses. Is buying and selling cat bonds really the 179 00:11:00,200 --> 00:11:03,640 Speaker 2: the best are the only use case for his risk model? 180 00:11:04,120 --> 00:11:07,520 Speaker 1: I think that I would say that cat bonds provide 181 00:11:07,520 --> 00:11:12,000 Speaker 1: a very important service in that they help ensure people 182 00:11:12,200 --> 00:11:18,200 Speaker 1: against catastrophic risk when traditional methods of protecting you, such 183 00:11:18,200 --> 00:11:23,360 Speaker 1: as insurance, aren't available. Last year, less than one third 184 00:11:23,640 --> 00:11:28,120 Speaker 1: of the three hundred and eighty billion dollars in global 185 00:11:28,200 --> 00:11:32,880 Speaker 1: climate losses was covered by insurance. So the developing world, 186 00:11:32,920 --> 00:11:40,239 Speaker 1: per countries we know, really at high risk from catastrophic 187 00:11:40,320 --> 00:11:43,720 Speaker 1: events driven by climate change. They have very little insurance, 188 00:11:44,280 --> 00:11:47,000 Speaker 1: but a CAT bond could provide that. So the World 189 00:11:47,000 --> 00:11:50,280 Speaker 1: Bank right now has about a billion dollars an issued 190 00:11:50,360 --> 00:11:54,400 Speaker 1: outstanding cat bonds for poorer countries, and it expects to 191 00:11:54,440 --> 00:11:57,560 Speaker 1: increase that to about five billion. So I think CAT 192 00:11:57,640 --> 00:12:01,600 Speaker 1: bonds will increasingly play a role in helping to sort 193 00:12:01,640 --> 00:12:03,160 Speaker 1: of bridge that gap a little bit. 194 00:12:06,320 --> 00:12:09,440 Speaker 2: This is the Big Take from Bloomberg News. I'm Sarah Holder. 195 00:12:09,760 --> 00:12:12,840 Speaker 2: This episode was produced by Adriana Tapia. It was edited 196 00:12:12,840 --> 00:12:15,679 Speaker 2: by Caitlin Kenny and Sharon Chen. It was mixed by 197 00:12:15,679 --> 00:12:19,319 Speaker 2: Ben O'Brien. It was fact checked by Naomi. Our senior 198 00:12:19,360 --> 00:12:23,400 Speaker 2: producers are Naomi Shavin and Elizabeth Ponso. Nicole Beemsterbor is 199 00:12:23,400 --> 00:12:26,840 Speaker 2: our executive producer. Sage Bauman is our head of podcasts. 200 00:12:27,360 --> 00:12:30,120 Speaker 2: Thanks for listening. Please follow and review The Big Take 201 00:12:30,200 --> 00:12:33,320 Speaker 2: wherever you listen to podcasts. It helps new listeners find 202 00:12:33,320 --> 00:12:36,200 Speaker 2: the show. We'll be back tomorrow.