Trader Insurance Quotes

Goldman Sachs itself—and so Goldman was in the position of selling bonds to its customers created by its own traders, so they might bet against them. Secondly, there was a crude, messy, slow, but acceptable substitute for Mike Burry’s credit default swaps: the actual cash bonds. According to a former Goldman derivatives trader, Goldman would buy the triple-A tranche of some CDO, pair it off with the credit default swaps AIG sold Goldman that insured the tranche (at a cost well below the yield on the tranche), declare the entire package risk-free, and hold it off its balance sheet. Of course, the whole thing wasn’t risk-free: If AIG went bust, the insurance was worthless, and Goldman could lose everything. Today Goldman Sachs is, to put it mildly, unhelpful when asked to explain exactly what it did, and this lack of transparency extends to its own shareholders. “If a team of forensic accountants went over Goldman’s books, they’d be shocked at just how good Goldman is at hiding things,

In other words, money isn’t a material reality – it is a psychological construct. It works by converting matter into mind. But why does it succeed? Why should anyone be willing to exchange a fertile rice paddy for a handful of useless cowry shells? Why are you willing to flip hamburgers, sell health insurance or babysit three obnoxious brats when all you get for your exertions is a few pieces of coloured paper? People are willing to do such things when they trust the figments of their collective imagination. Trust is the raw material from which all types of money are minted. When a wealthy farmer sold his possessions for a sack of cowry shells and travelled with them to another province, he trusted that upon reaching his destination other people would be willing to sell him rice, houses and fields in exchange for the shells. Money is accordingly a system of mutual trust, and not just any system of mutual trust: money is the most universal and most efficient system of mutual trust ever devised. What created this trust was a very complex and long-term network of political, social and economic relations. Why do I believe in the cowry shell or gold coin or dollar bill? Because my neighbours believe in them. And my neighbours believe in them because I believe in them. And we all believe in them because our king believes in them and demands them in taxes, and because our priest believes in them and demands them in tithes. Take a dollar bill and look at it carefully. You will see that it is simply a colourful piece of paper with the signature of the US secretary of the treasury on one side, and the slogan ‘In God We Trust’ on the other. We accept the dollar in payment, because we trust in God and the US secretary of the treasury. The crucial role of trust explains why our financial systems are so tightly bound up with our political, social and ideological systems, why financial crises are often triggered by political developments, and why the stock market can rise or fall depending on the way traders feel on a particular morning.

The Proofs Human society has devised a system of proofs or tests that people must pass before they can participate in many aspects of commercial exchange and social interaction. Until they can prove that they are who they say they are, and until that identity is tied to a record of on-time payments, property ownership, and other forms of trustworthy behavior, they are often excluded—from getting bank accounts, from accessing credit, from being able to vote, from anything other than prepaid telephone or electricity. It’s why one of the biggest opportunities for this technology to address the problem of global financial inclusion is that it might help people come up with these proofs. In a nutshell, the goal can be defined as proving who I am, what I do, and what I own. Companies and institutions habitually ask questions—about identity, about reputation, and about assets—before engaging with someone as an employee or business partner. A business that’s unable to develop a reliable picture of a person’s identity, reputation, and assets faces uncertainty. Would you hire or loan money to a person about whom you knew nothing? It is riskier to deal with such people, which in turn means they must pay marked-up prices to access all sorts of financial services. They pay higher rates on a loan or are forced by a pawnshop to accept a steep discount on their pawned belongings in return for credit. Unable to get bank accounts or credit cards, they cash checks at a steep discount from the face value, pay high fees on money orders, and pay cash for everything while the rest of us enjoy twenty-five days interest free on our credit cards. It’s expensive to be poor, which means it’s a self-perpetuating state of being. Sometimes the service providers’ caution is dictated by regulation or compliance rules more than the unwillingness of the banker or trader to enter a deal—in the United States and other developed countries, banks are required to hold more capital against loans deemed to be of poor quality, for example. But many other times the driving factor is just fear of the unknown. Either way, anything that adds transparency to the multi-faceted picture of people’s lives should help institutions lower the cost of financing and insuring them.

If Jim was back at the imaginary dinner party, trying to explain what he did for a living, he'd have tried to keep it simple: clearing involved everything that took place between the moment someone started at trade — buying or selling a stock, for instance — and the moment that trade was settled — meaning the stock had officially and legally changed hands. Most people who used online brokerages thought of that transaction as happening instantly; you wanted 10 shares of GME, you hit a button and bought 10 shares of GME, and suddenly 10 shares of GME were in your account. But that's not actually what happened. You hit the Buy button, and Robinhood might find you your shares immediately and put them into your account; but the actual trade took two days to complete, known, for that reason, in financial parlance as 'T+2 clearing.' By this point in the dinner conversation, Jim would have fully expected the other diners' eyes to glaze over; but he would only be just beginning. Once the trade was initiated — once you hit that Buy button on your phone — it was Jim's job to handle everything that happened in that in-between world. First, he had to facilitate finding the opposite partner for the trade — which was where payment for order flow came in, as Robinhood bundled its trades and 'sold' them to a market maker like Citadel. And next, it was the clearing brokerage's job to make sure that transaction was safe and secure. In practice, the way this worked was by 10:00 a.m. each market day, Robinhood had to insure its trade, by making a cash deposit to a federally regulated clearinghouse — something called the Depository Trust & Clearing Corporation, or DTCC. That deposit was based on the volume, type, risk profile, and value of the equities being traded. The riskier the equities — the more likely something might go wrong between the buy and the sell — the higher that deposit might be. Of course, most all of this took place via computers — in 2021, and especially at a place like Robinhood, it was an almost entirely automated system; when customers bought and sold stocks, Jim's computers gave him a recommendation of the sort of deposits he could expect to need to make based on the requirements set down by the SEC and the banking regulators — all simple and tidy, and at the push of a button.

Social money and payments: iZettle, Payatrader, mPowa, SumUp, payleven, Inuit GoPayment, Square •   Social lending and saving: Zopa, RateSetter, smava, Prosper, Lending Club, Cashare •   Social insurance: Friendsurance •   Social investing and trading: StockTwits, eToro, Myfxbook, Fxstat, MetaTrader Trade Signals, Collective2, Tradeo, ZuluTrade, Nutmeg •   Social trade financing: MarketInvoice, Platform Black, the Receivables Exchange, Urica •   Payday Lending: Wonga, Cash America, Advance America •   Goal setting and gamification: SmartyPig, Moven, Simple •   Crowdfunding: Funding Circle, Kickstarter, Indiegogo, crowdrise, Razoo

In a now famous experiment they found that the majority of people, whether predictors or nonpredictors, will judge a deadly flood (causing thousands of deaths) caused by a California earthquake to be more likely than a fatal flood (causing thousands of deaths) occurring somewhere in North America (which happens to include California). As a derivatives trader I noticed that people do not like to insure against something abstract; the risk that merits their attention is always something vivid.

The primary reason for the development of trade and traders was to introduce and spread this religion more quickly, and to insure that the introduction of new techniques and a new economy would be subject to our thorough and intimate control.

By now, though, it had been a steep learning curve, he was fairly well versed on the basics of how clearing worked: When a customer bought shares in a stock on Robinhood — say, GameStop — at a specific price, the order was first sent to Robinhood's in-house clearing brokerage, who in turn bundled the trade to a market maker for execution. The trade was then brought to a clearinghouse, who oversaw the trade all the way to the settlement. During this time period, the trade itself needed to be 'insured' against anything that might go wrong, such as some sort of systemic collapse or a default by either party — although in reality, in regulated markets, this seemed extremely unlikely. While the customer's money was temporarily put aside, essentially in an untouchable safe, for the two days it took for the clearing agency to verify that both parties were able to provide what they had agreed upon — the brokerage house, Robinhood — had to insure the deal with a deposit; money of its own, separate from the money that the customer had provided, that could be used to guarantee the value of the trade. In financial parlance, this 'collateral' was known as VAR — or value at risk. For a single trade of a simple asset, it would have been relatively easy to know how much the brokerage would need to deposit to insure the situation; the risk of something going wrong would be small, and the total value would be simple to calculate. If GME was trading at $400 a share and a customer wanted ten shares, there was $4000 at risk, plus or minus some nominal amount due to minute vagaries in market fluctuations during the two-day period before settlement. In such a simple situation, Robinhood might be asked to put up $4000 and change — in addition to the $4000 of the customer's buy order, which remained locked in the safe. The deposit requirement calculation grew more complicated as layers were added onto the trading situation. A single trade had low inherent risk; multiplied to millions of trades, the risk profile began to change. The more volatile the stock — in price and/or volume — the riskier a buy or sell became. Of course, the NSCC did not make these calculations by hand; they used sophisticated algorithms to digest the numerous inputs coming in from the trade — type of equity, volume, current volatility, where it fit into a brokerage's portfolio as a whole — and spit out a 'recommendation' of what sort of deposit would protect the trade. And this process was entirely automated; the brokerage house would continually run its trading activity through the federal clearing system and would receive its updated deposit requirements as often as every fifteen minutes while the market was open. Premarket during a trading week, that number would come in at 5:11 a.m. East Coast time, usually right as Jim, in Orlando, was finishing his morning coffee. Robinhood would then have until 10:00 a.m. to satisfy the deposit requirement for the upcoming day of trading — or risk being in default, which could lead to an immediate shutdown of all operations. Usually, the deposit requirement was tied closely to the actual dollars being 'spent' on the trades; a near equal number of buys and sells in a brokerage house's trading profile lowered its overall risk, and though volatility was common, especially in the past half-decade, even a two-day settlement period came with an acceptable level of confidence that nobody would fail to deliver on their trades.

All human beings, according to the conception of the Vedic seers, form the physical body of the Purusha, or Cosmic Person. The spiritual men form, as it were, his head, the warriors His arms, the merchants and traders His thighs and the labourers His feet. A healthy co-ordination among these four classes of people sustains the strength and the well-being of a society, as a harmony among the four principal physical parts insures the strength and well-being of a body. (p. 30)

Koch Agriculture first branched out into the beef business, and it did so in a way that gave it control from the ranch to the butcher’s counter. Koch bought cattle feedlots. Then it developed its own retail brand of beef called Spring Creek Ranch. Dean Watson oversaw a team that worked to develop a system of “identity preservation” that would allow the company to track each cow during its lifespan, allowing it over time to select which cattle had the best-tasting meat. Koch held blind taste tests of the beef it raised. Watson claimed to win nine out of ten times. Then Koch studied the grain and feed industries that supplied its feedlots. Watson worked with experts to study European farming methods because wheat farmers in Ukraine were far better at raising more grain on each acre of land than American farmers were. The Europeans had less acreage to work with, forcing them to be more efficient, and Koch learned how to replicate their methods. Koch bought a stake in a genetic engineering company to breed superyielding corn. Koch Agriculture extended into the milling and flour businesses as well. It experimented with building “micro” mills that would be nimbler than the giant mills operated by Archer Daniels Midland and Cargill. Koch worked with a start-up company that developed a “pixie dust” spray preservative that could be applied to pizza crusts, making crusts that did not need to be refrigerated. It experimented with making ethanol gasoline and corn oil. There were more abstract initiatives. Koch launched an effort to sell rain insurance to farmers who had no way to offset the risk of heavy rains. To do that, Koch hired a team of PhD statisticians to write formulas that correlated corn harvests with rain events, figuring out what a rain insurance policy should cost. At the same time, Koch’s commodity traders were buying contracts for corn and soybeans, learning more every day about those markets.

While traders might have seen what was coming, it appeared that the general public did not. O’Neill saw a gap in the market in early 2000. A giant gap. The price of gas options was cheap—too cheap to account for what was apparently coming down the road. In other words, the insurance policies against a sudden price spike were not as expensive as they ought to have been. So O’Neill started snapping up the options and holding on to them, knowing that they would become more valuable. As usual, he wasn’t just making a bet that prices were going to go up. He was primarily betting that markets were about to become more volatile. He built up a large position with his natural gas options and underliers that was “long volatility,” meaning that he bet volatility would increase. He assumed that the positions would provide a good return for Koch Industries. He was wrong. He grossly underestimated the riches that the coming volatility was about to deliver. Senior executives in Koch Supply & Trading realized that they could no longer pay their traders like engineers. There was a competition for talent, and too many well-trained people were bleeding off the Koch trading floor. There was one person who seemed to resist big paydays for the traders: Charles Koch. The business failures of the 1990s impressed on Charles Koch the need for humility among his workforce. The thinking went that it was the high-flying ambition and loose planning that led to many of the business losses at Purina Mills.

As a derivatives trader I noticed that people do not like to insure against something abstract; the risk that merits their attention is always something vivid.

The numbers shocked even him. They didn't need to collapse; they merely needed to stop rising so fast. House prices were still rising, and yet default rates were approaching 4 percent; if they rose to just 7 percent, the lowest investment-grade bonds, rated triple-B-minus, went to zero. If they rose to 8 percent, the next lowest-rated bonds, rated triple-B, went to zero. At that moment--in November 2005--Greg Lippmann realized that he didn't mind owning a pile of credit default swaps on subprime mortgage bonds. They weren't insurance; they were a gamble; and he liked the odds. He wanted to be short.

A lot of applications of alternative data are being found today in insurance and credit markets (see e.g. Turner, 2008; Turner, 2011; Financial Times, 2017).